Trading Range Breakout Research Articles

Algorithmic trading using combinational rule vector and deep reinforcement learning

Algorithmic trading rules are widely used in financial markets as technical analysis tools for security trading. However, traditional trading rules are not sufficient to make a trading decision. In this paper, we propose a new algorithmic trading method called CR-DQN, which incorporates deep Q-learning with two popular trading rules: moving average (MA) and trading range break-out (TRB). The input of deep Q-learning is combinational rule vectors, whose component is a linear combination of 140 rules produced by MA and TRB with different parameters. Due to non-stationary characteristics, we devise a reward driven combination weight updating scheme to generate combinational rule vectors, which can capture intrinsic features of financial data. Since the sparse reward exists in CR-DQN, we design a piecewise reward function which shows great potential in the experiments. Taking combinational rule vectors as input, the LSTM based Deep Q-learning network is used to learn an optimal algorithmic trading strategy. We apply our model to both Chinese and non-Chinese stock markets, and CR-DQN exhibits the best performance on a variety of evaluation criteria compared to many other approaches, demonstrating the effectiveness of the proposed method.

Be greedy when others are fearful: Evidence from a two-decade assessment of the NDX 100 and S&P 500 indexes

This study explores the profitability of trading range breakout (TRB) trading rules and commonly used moving average (MA) trading rules in the NDX 100 and S&P 500 indices. Its objective is to offer practical insights for investors who own QQQ and SPY, two popular ETFs that track these two indices. Using big data analytics, the study reveals that investors who trade and hold these two ETFs can potentially profit from using TRB instead of MA trading rules. Unexpectedly as opposed to buying signals from TRB trading rules or momentum MA trading rules, investors may obtain much higher returns by purchasing these ETFs (i.e., QQQ and SPY) as selling signals emitted by TRB rules, particularly by buying these ETFs as stock prices break below 150-day low and 200-day low, a situation in which many investors may experience panic due to the continuously falling share price. As such, we argue that our findings may suggest that being greedy when others are fearful (i.e., the Warren Buffett quote) is of significance to this study.

Further evidence on the returns to technical trading rules: Insights from fourteen currencies

This work studies the returns to technical trading rules utilising a sample of 14 currency pairs by applying five trading rules: Filter rules, Trading Range Breakout, Moving Average, and Bollinger bands over varying market conditions. The mixed results indicate that technical analysis based on the applied trading rules is appropriate for the trading strategies examined. The one per cent filter, 150-day MA rule and the Bollinger bands trading rule surpassed the other applied techniques in general performance. What is also clear is that these trading rules are rewarding over varying market conditions. Results from various subperiods, representing normal and stressful market conditions, generally confirm our main findings and show the added value of the trading range breakout rule, which delivers performance in strongly trending markets. Evidence from bootstrap analysis demonstrates the predictive ability of the Moving Average and Bollinger band trading rules.

A hybrid multicategory framework for generating automated trading systems

AbstractThis work proposes a framework for producing automatic trading systems that suggest making trading decisions in financial markets. The trading systems thus generated utilize multicategory classifiers. A collection of important technical indicators are considered as input features for the underlying multicategory classifiers. The proposed trading systems also have an option to use random forest (RF) algorithms for feature selection. The trading range breakout strategy is used to train the proposed trading systems thus generating “BUY/SELL/WAIT” trading signals on daily open prices. The performances of the proposed trading systems are evaluated over five future indices. Empirical findings suggest that the day trading systems based on the proposed multicategory SVM classifiers along with the RF technique outperform the day trading systems built using the multicategory classifiers taken from the literature. The trading range breakout strategy‐based systems are found to be superior to the traditional BUY‐HOLD strategy and that of RF‐PSVM (BUY/SELL) based strategy.

Can exchange-traded funds be profitably traded with the trading range breakout technical trading rule?

This study investigates whether the trading range breakout technical trading rule can be applied in exchange-traded funds. Using the samples of crude oil exchange-traded funds, namely USO, USL, UCO, and DBO, that track West Texas intermediate (WTI), the results indicate that the trading range breakout technical trading rule outperforms the benchmark buy-and-hold strategy. In addition, a volatility-based trading range breakout technical trading rule is proposed to enhance trading performance. The results suggest that the proposed TGARCH-MA can be used as a technical trading rule to assist investors and fund managers interested in using the trading range breakout in exchange-traded funds.

The Predictability and profitability of Trading Range Break-out Trading Rules: Evidence from Pakistan Stock Exchange

The efficient market hypothesis has been one of themost extensively researched topics in the academic literature for decades. An implication ofweak form of efficiency is that the technical trading rules will not produce abnormal returns. The purpose of this research is to analyze findings of application of trading range breakout test on daily closing share prices of 100 companies listed on a Pakistan Stock Exchange over ten years from 2006 to 2015,thus examining its efficiency at the weak form. The results show strong support for trading range break-out rules having both predictability and profitability for PSX. It refers that the returns from these rules are not same as investors earn from a naïve buy and hold strategy. The uses of the trading range break-out rules produce abnormal returns to investors and hence nullify the weak form of efficiency on PSX.

The Predictability and Profitability of Simple Moving Averages and Trading Range Breakout Rules in the Pakistan Stock Market

This paper inspects whether variable- and fixed-length moving averages (VMA and FMA), and trading range breakout (TRB) rules have prognostic capability and can earn profits superior to buy-and-hold plan, when applied on KSE-100 index of Pakistan stock market during the full sample period January 1, 1997 to December 31, 2013. Full sample results provided empirical evidence for VMA rule that it has significant predictive power and is able to generate profits superior to simple buy-and-hold plan even after inclusion of transaction costs. The highest mean buy returns yielded by VMA, FMA and TRB rules are seen in noncrises periods. The overall implication of this study is that traders in the Pakistan stock market can utilize this information to obtain excess returns on a regular basis.

The profitability of technical trading rules in the Bitcoin market

We apply seven trend-following indicators to assess the profitability of technical trading rules in the Bitcoin market. Using daily price data from July 2010 to January 2019, our main results show that specific technical analysis trading rules, mainly trading range breakout, contain significant forecasting power for Bitcoin prices, allowing the outperformance of the buy-and-hold strategy through the Sharpe ratio computed via the bootstrapping method. Results from various sub-periods, representing normal and boom markets, generally confirm our main finding and show that the added value of the trading range breakout rule delivers outperformance in strongly trending markets.

The effectiveness of technical trading rules in cryptocurrency markets

We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.

The Effectiveness of Technical Trading Rules in Cryptocurrency Markets

We analyse various technical trading rules in the form of the moving average-oscillator and trading range break-out strategies to specifically test resistance and support levels and their trading performance using high-frequency Bitcoin returns. Overall, our results provide significant support for the moving average strategies. In particular, variable-length moving average rule performs the best with buy signals generating higher returns than sell signals.

Predictability of Technical Analysis on Singapore Stock Market, Before and After the Asian Financial Crisis

We investigate the predictability of technical analysis applied to the Singapore Straits Times Index. There are three trading rules – the Simple Moving Average, the Dual Moving Average and Trading Range Breakout with and without a one percent band – which are applied to undertake the prediction. Our findings conclude that the three indicators with and without the one percent band applied generate significantly positive returns for both the buy and sell signals in the periods before and after the 1997-98 Asian Financial Crisis coving bull run and bear markets and mixed markets. Our findings conclude that Singapore stock market is not as efficient as US stock market.

Bootstrap testing of trading strategies in emerging balkan stock markets

Most lately, the attention of technical trading analysis has shifted to emerging stock markets which collectively bring a signifi cant alternative source of opportunities to international investors. Accordingly, the aim of this paper is to investigate the effectiveness of four technical trading rules (moving average, fi lter, trading range breakout and channel breakout rule) in six stock market indices of the Balkan States. Also, the paper is providing resume evidence on the predictive power of four mentioned trading rules. We apply the Reality Check and the Superior Predictive Ability test using bootstrap methodology to evaluate the relative performance of those rules. Furthermore, presented tests provide an answer to data snooping problems, which is essential to obtain unbiased outcomes. The original time series is resampled with random draw in two ways: a parametric residual-based method from the AR(1)-GARCH(1,1) model, and a nonparametric, the moving block bootstrap. After including data snooping biases, this study fi nds that the null hypothesis that trading rules do not outperform the benchmark can be rejected at the 5 percent signifi cance level for fi ve separate stock indices, excluding the MBI10 index. Similarly, such results show the rejection of the weak-form market effi ciency hypothesis in case of mentioned stock markets. Applied technical trading rule algorithms in all six stock market indices mainly generate more losing trades then wining trades. Finally, transaction costs have relatively small effect on the overall performance of selected technical trading rules in case of indices BELEX15, CROBEX, SBITOP and MONEX20, but with some changes in choice of the best technical trading rule considering the effects of trading frequencies.

Trading Range Breakout Test on Daily Stocks of Indian Markets

In the financial literature Efficient Market Hypothesis (EMH) has been one of the dominant topics. An implication of weak-form of efficiency/random walk is that the trading rules will not generate economic profits. The purpose of this study is to analyze results of application of trading range breakout (TRB) test on daily stock prices of Indian Markets, thus investigating its efficiency at the weak form level (Fama,1970). The results from the trading rule tests indicated that the technical trading rules do not yield statistically significant forecasting power. It means that forecasting of returns based on trading rules cannot be employed to earn abnormal returns.

Weak Form of Market Efficiency Trading Range Breakout Test on Weekly Stocks of India Markets

Weak Form of Market Efficiency Trading Range Breakout Test on Weekly Stocks of India Markets

Trading profitability from learning and adaptation on the Tokyo Stock Exchange

This study proposes unexamined technical trading rules, which are dynamically switching strategies among filter, moving average and trading-range breakout rules. The dynamically switching strategy is formulated based on a discrete choice theory consistent with the concept of myopic utility maximization. We utilize the transaction data of the individual stocks listed on the Nikkei 225 from September 1, 2005 to August 31, 2007. We demonstrate that switching strategies produce positive returns and their performance is better than those from the buy-and-hold and non-switching strategies over our sample periods. We also demonstrate equivalent performance for switching with different learning horizons, implying that behavioural heterogeneity of stock investors arises from the coexistence of different strategies with varying degrees of learning horizons. Our result supports several research assumptions and results on agent-based theoretical models that successfully replicate empirical features in financial markets, such as fat tails of return distributions and volatility clustering. However, upon considering the effects of data-snooping bias superior performance disappears.

Technical market indicators: An overview

Current evidence on the predictability of technical analysis largely concentrates on price-based technical indicators such as moving averages rules and trading range breakout rules. In contrast, the predictability of widely used technical market indicators such as advance/decline lines, volatility indices, and short-term trading indices has drawn limited attention. Although some market indicators have also become popular sentiment proxies in the behavioral finance field to predict returns, the results generally rely on using just one or a few indicators at a time. This approach raises the risk of data snooping, since so many proxies are proposed. We review and examine the profitability of a wide range of 93 market indicators. We give these technical market indicators the benefit of the doubt, but even then we find little evidence that they predict stock market returns. This conclusion continuously holds even if we allow predictability to be state dependent on business cycles or sentiment regimes.

Technical Market Indicators: An Overview

Current evidence on the predictability of technical analysis largely concentrates on price-based technical indicators such as moving averages rules and trading range breakout rules. In contrast, the predictability of widely used technical market indicators such as advance/decline lines, volatility indices, and short-term trading indices has drawn limited attention. Although some market indicators have also become popular sentiment proxies in the behavioral finance field to predict returns, the results generally rely on using just one or a few indicators at a time. This approach raises the risk of data snooping, since so many proxies are proposed. We review and examine the profitability of a wide range of 93 market indicators. We give these technical market indicators the benefit of the doubt, but even then we find little evidence that they predict stock market returns. This conclusion continuously holds even if we allow predictability to be state dependent on business cycles or sentiment regimes.

Combining technical trading rules using particle swarm optimization

Technical trading rules have been utilized in the stock market to make profit for more than a century. However, only using a single trading rule may not be sufficient to predict the stock price trend accurately. Although some complex trading strategies combining various classes of trading rules have been proposed in the literature, they often pick only one rule for each class, which may lose valuable information from other rules in the same class. In this paper, a complex stock trading strategy, namely performance-based reward strategy (PRS), is proposed. PRS combines the two most popular classes of technical trading rules – moving average (MA) and trading range break-out (TRB). For both MA and TRB, PRS includes various combinations of the rule parameters to produce a universe of 140 component trading rules in all. Each component rule is assigned a starting weight, and a reward/penalty mechanism based on rules’ recent profit is proposed to update their weights over time. To determine the best parameter values of PRS, we employ an improved time variant particle swarm optimization (TVPSO) algorithm with the objective of maximizing the annual net profit generated by PRS. The experiments show that PRS outperforms all of the component rules in the testing period. To assess the significance of our trading results, we apply bootstrapping methodology to test three popular null models of stock return: the random walk, the AR(1) and the GARCH(1,1). The results show that PRS is not consistent with these null models and has good predictive ability.

Predictive ability and profitability of simple technical trading rules: Recent evidence from Southeast Asian stock markets

This study investigates whether the moving average and trading range breakout rules can forecast stock price movements and outperform a simple buy-and-hold strategy after adjusting for transaction costs over the period from January 1991 to December 2008. The empirical results show that the trading rules have stronger predictive power in the emerging stock markets of Malaysia, Thailand, Indonesia, and the Philippines than in the more developed stock market of Singapore consistent with earlier studies. In addition, the short-term variants of the technical trading rules have better predictive ability than long-term variants. However, unlike earlier studies we show that transaction costs can eliminate the trading profits implying weak-form efficiency in most stock markets during our study period further suggesting that these markets have become more informationally efficient over time. Our results highlight the need to constantly revisit statements about the efficiency of economically dynamic and rapidly growing emerging stock markets.

Trading rules and stock returns: some further short run evidence from the Hang Seng 1997–2008

This article re-examines the work of Coutts and Cheung (2000), who investigated the applicability and validity of trading rules in the Hang Seng Index (HSI) on the Hong Kong Stock Exchange (HKSE) for the period January 1985 through June 1997, and for two subsamples of equal length, partitioned from the whole sample. They concluded that the moving average oscillator and the trading range break-out rules, appeared to be present, to varying extents, for all three data samples, although the Trading Range Break-out rule was by far the strongest. However, their striking conclusion was that these rules were statistically significant over much shorter data periods than used in previous studies. They also suggested that because there is a tendency for potentially ‘profitable’ trading rules, once documented to cease existing, and consequently further research concerning the HSI was required in years hence. It is in this spirit that we replicate the work of Coutts and Cheung, and conclude that these once potentially...