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Japanese Candlestick Trading Strategies: A Review Of Literature

Prasad Kulkarni, Dr. Murugaiah V
2018 Zenodo  
The purpose of this study is to review the evidence on the performance of Japanese candlestick trading strategies.  ...  To achieve this, the study comprehensively reviews survey, theoretical and empirical studies on the subject of candlestick trading strategies and discusses the consistency and reliability of candlestick  ...  Candlestick trading strategies can be used as standalone basis or in combination with some technical tools / indicators for making short-term investment decisions.  ... 
doi:10.5281/zenodo.1323120 fatcat:cm3z5kr3cna3tg6ewy36pxa3o4

ML-Based Interconnected Affecting Factors with Supporting Matrices for Assessment of Risk in Stock Market

Bhupinder Singh, Santosh Kumar Henge, Amit Sharma, C. Menaka, Pawan Kumar, Sanjeev Kumar Mandal, Baru Debtera, Kalidoss Rajakani
2022 Wireless Communications and Mobile Computing  
The goal of this study was to fill this gap by utilizing the body of existing research on stock index forecasting combined with machine learning techniques for both short- and long-term risk managements  ...  In today's world, people study and evaluate trading stocks to make informed decisions, based on available financial data and market information.  ...  Compared to long-term investments or even short-term trades, intraday trading carries a greater risk.  ... 
doi:10.1155/2022/2432839 fatcat:sx2onc27q5a3hmgox7f2qnod2m

Investigating Algorithmic Stock Market Trading using Ensemble Machine Learning Methods

Ramzi Saifan, Khaled Sharif, Mohammad Abu-Ghazaleh, Mohammad Abdel-Majeed
2020 Informatica (Ljubljana, Tiskana izd.)  
These results strengthen the role of ensemble method based machine learning in automated stock market trading.  ...  All methods are trained using multiple technical indicators and automatic stock selection is used.  ...  If one classifier is used, the algorithm will long or short based on how sure the 6 The fundamental data of a stock is in the broadest terms any data, besides the trading patterns of the stock itself  ... 
doi:10.31449/inf.v44i3.2904 fatcat:xbbtmuzmrba3lfstaw7tkjn7d4

Strategy to Prevent the Risk of Trading in Binary Options

María Elena Espín Oleas, Mariela Elizabeth Arévalo Palacios
2019 China-USA Business Review  
The article develops a strategy to diminish the risk in these transactions, based on a bibliographical revisation, statistic, with a qualitative methodology, investigation-action that involves the actions  ...  The world of the trading offers a group of very structured economic rules starting from which it is operated in bag or in binary options being that bigger risk represents for the investment.  ...  It is mainly used to determine the future short-term price of the trend of a certain financial asset. The parabolic SAR indicator allows traders to determine stop loss orders.  ... 
doi:10.17265/1537-1514/2019.01.003 fatcat:x5yo2y2s4ng7vp55crgqazgm4i

A Channeled Multilayer Perceptron as Multi-Modal Approach for Two Time-Frames Algo-Trading Strategy

Noussair Fikri, Khalid Moussaid, Mohamed Rida, Amina El Omri, Noureddine Abghour
2022 International Journal of Advanced Computer Science and Applications  
It is based on neural networks applying triple exponential weighted moving average (EMA) as a trend indicator, Bollinger bands as a volatility indicator, and stochastic RSI as a momentum reversal indicator  ...  modalities (Channel) has its That stands for a dynamic channel coefficient to produce a multi-processed feed-forward neural network that prevents uncertain trading signals depending on trend-volatility-momentum  ...  This technique can be effective on trend following or momentum reversal but is not adaptable to volatile markets; the S&P 500 is a long-term trading index.  ... 
doi:10.14569/ijacsa.2022.0130259 fatcat:s5zisuzvlrabfapzilikfca5oq

Trading in financial markets using pattern recognition optimized by genetic algorithms

Paulo Parracho, Rui Neves, Nuno Horta
2010 Proceedings of the 12th annual conference comp on Genetic and evolutionary computation - GECCO '10  
the stock exchange, making use of a strategy averse to risk, based on technical analysis or fundamental.  ...  Figure 8 - 8 Return of best GA and B&H for all the 100 stocks. Table 1 . 1 Profitability for Different Stock Indexes.  ... 
doi:10.1145/1830761.1830884 dblp:conf/gecco/ParrachoNH10 fatcat:6nsmewsqbff2vfbzwchkctrs7i

Stock Price Forecasting Based on Wavelet Filtering and Ensembled Machine Learning Model

Pengyue Wang, Xuesheng Li, Zhiliang Qin, Yuanyuan Qu, Zhongkai Zhang
2022 Mathematical Problems in Engineering  
In stock price analysis, the prediction of short-term movements is of much interest to investors and traders.  ...  Numerical results on the Shanghai composite index show that the proposed approach has noticeable advantages over traditional statistical and machine learning methods when predicting near term price trends  ...  A commonly used indicator is known as the KDJ index [21] , which is otherwise known as the random index. It is a practical technical indicator that is commonly used in short-term trend analysis.  ... 
doi:10.1155/2022/4024953 doaj:c7a72c3192c5439c8e4ff9f0bb9ff328 fatcat:jwczdvzdvrcztlm4halcx3iyty

Collective dynamics of stock market efficiency [article]

Luiz G. A. Alves, Higor Y. D. Sigaki, Matjaz Perc, Haroldo V. Ribeiro
2020 arXiv   pre-print
We thus propose a network representation of stock markets that aggregates their short-term efficiency patterns into a global and coherent picture.  ...  However, we also show that efficiency ranks and clusters of markets with similar trends are only stable for a few months at a time.  ...  Financial network of stock markets exhibiting similar short-term trends of efficiency.  ... 
arXiv:2011.14809v1 fatcat:imk6dk6xn5d33a4kmtl4blq6ki

Stock Price Movement Prediction Based on a Deep Factorization Machine and the Attention Mechanism

Xiaodong Zhang, Suhui Liu, Xin Zheng
2021 Mathematics  
Further, in data representation, we used the sub-industry index as supplementary information for the current state of the stock, since there exists stock price co-movement between individual stocks and  ...  In this paper, we constructed a convolutional neural network model based on a deep factorization machine and attention mechanism (FA-CNN) to improve the prediction accuracy of stock price movement via  ...  For short-term stock forecasting, the optimal time window size can help the model achieve a better result.  ... 
doi:10.3390/math9080800 fatcat:lphqvfrzf5gyhdytupttbnxpdi

Stock Prediction Based on Technical Indicators Using Deep Learning Model

Manish Agrawal, Piyush Kumar Shukla, Rajit Nair, Anand Nayyar, Mehedi Masud
2022 Computers Materials & Continua  
The analysis of the dataset of three most popular banking organizations obtained from the live stock market based on the National Stock exchange (NSE) -India, a Long Short Term Memory (LSTM) is used.  ...  Stock market trends forecast is one of the most current topics and a significant research challenge due to its dynamic and unstable nature.  ...  Acknowledgement: We are very thankful to RGPV, Jagran Lakecity University, Taif University Researchers Supporting Project Number (TURSP-2020/10) and Duy Tan University for their continuous support and  ... 
doi:10.32604/cmc.2022.014637 fatcat:qvxkfghtqjdgbournwwx4gl4ku

Intraday Trading Strategy based on Gated Recurrent Unit and Convolutional Neural Network: Forecasting Daily Price Direction

Nabil MABROUK, Marouane CHIHAB, Zakaria HACHKAR, Younes CHIHAB
2022 International Journal of Advanced Computer Science and Applications  
In this article, we propose a trading strategy based on machine learning algorithms to reduce the risks of trading on the forex market and increase benefits at the same time.  ...  Forex or FX is the short form of the Foreign Exchange Market, it is known as the largest financial market in the world where Investors can buy a certain amount of currency and hold it on until the exchange  ...  The goal of this work is to create a good strategy for day trading, and for that, we have used an OLHCV data, indexed on the timestamp one day; each row is a one-day observation of five variables: Open  ... 
doi:10.14569/ijacsa.2022.0130369 fatcat:hbm7u7nr4rfipd5uyfezh2znce

The Profitability of Technical Analysis: A Review

Cheol-Ho Park, Scott H. Irwin
2004 Social Science Research Network  
Early studies indicated that technical trading strategies were profitable in foreign exchange markets and futures markets, but not in stock markets before the 1980s.  ...  Future research must address these deficiencies in testing in order to provide conclusive evidence on the profitability of technical trading strategies. ii  ...  The Dual Moving Average Crossover system generates trading signals by identifying when the short-term trend rises above or below the long-term trend. Specifications of the system are as follows: A.  ... 
doi:10.2139/ssrn.603481 fatcat:765a2jvt7ncalkrjyvsjmy72s4


Hesham I. Almujamed, Ghassan H. Mardini, Mahmoud M. Salama
2015 Studies in Business and Economics  
The empirical results indicate that moving average strategies are successful in predicting the returns for the ASE Index and outperforming the naive buy-and-hold strategy.  ...  The research uses statistical analyses and moving average rules and offers further evidence of the inefficiency of the Amman stock market when applying trading rules.  ...  INTRODUCTION The current study investigates whether future price changes for a market index can be predicted effectively based on historical data.  ... 
doi:10.29117/sbe.2015.0083 fatcat:qr2o5feqnjhbplpn2zh4remg5a

Crude oil futures trading and uncertainty

Robert L. Czudaj
2019 Energy Economics  
We consider two concepts of uncertainty and two momentum trading indicators based on technical analysis.  ...  Our findings indicate that both measures of uncertainty affect momentum trading on the crude oil futures market in several periods, especially during the great recession between 2007 and 2009.  ...  trading to uncertainty and especially to analyze if trading signals due to uncertainty shocks differ for short-term and for long-term investors, we have provided the same analysis at a disaggregated level  ... 
doi:10.1016/j.eneco.2019.01.002 fatcat:k2t2m2n26rdiromkpismnqnkae

QF-TraderNet: Intraday Trading via Deep Reinforcement With Quantum Price Levels Based Profit-And-Loss Control

Yifu Qiu, Yitao Qiu, Yicong Yuan, Zheng Chen, Raymond Lee
2021 Frontiers in Artificial Intelligence  
This paper introduced an end-to-end RL intraday trading agent, namely QF-TraderNet, based on the quantum finance theory (QFT) and deep reinforcement learning.  ...  QF-TraderNet composes two neural networks: 1) A long short term memory networks for the feature learning of financial time series; 2) a policy generator network (PGN) for generating the distribution of  ...  FCM , a forecasting model based on RNN trend predictor, consisting of a 7-layer LSTM with 512 hidden dimensions. It trades with a Buy-Winner-Sell-Loser strategy. RF.  ... 
doi:10.3389/frai.2021.749878 pmid:34778753 pmcid:PMC8586520 fatcat:p6bncxg4vbd3lk6l56zbqsmk7q
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