scholarly journals Portfolio Management Using Artificial Trading Systems Based on Technical Analysis

10.5772/38213 ◽  
2012 ◽  
Author(s):  
Massimiliano Kaucic
Keyword(s):  
Portfolio Management ◽  
Technical Analysis ◽  
Trading Systems

scholarly journals The Effectiveness of the Transaction Systems on the Dax Index

2020 ◽  
Vol 4 (28) ◽  
pp. 149-174
Author(s):  
Marek Trembiński ◽  
Joanna Stawska
Keyword(s):  
Portfolio Management ◽  
Stock Exchange ◽  
Technical Analysis ◽  
Quality Measures ◽  
Daily Basis ◽  
Significant Loss ◽  
Investment Strategies ◽  
Trading Systems ◽  
Management Quality ◽  
Analysis Tools

The purpose of the article/hypothesis: The aim of this article is to examine the effectiveness of trading systems built on the basis of technical analysis tools in 2015–2020 on the DAX stock exchange index. Efficiency is understood as generating positive rates of return, taking into account the risk incurred by the investor, as well as achieving better results than passive strategies. Presenting empirical evidence implying the value of technical analysis is a difficult task not only because of a huge number of instruments used on a daily basis, but also due to their almost unlimited possibility to modify parameters and often subjective evaluation.Methodology: The effectiveness of technical analysis tools was tested using selected investment strategies based on oscillators and indicators following the trend. All transactions were carried out on the Meta Trader 4 platform. The analyzed strategies were comprehensively assessed using the portfolio management quality measures, such as the Sharpe measure or the MAR ratio (Managed Account Ratio).Results of the research: The test results confirmed that the application of described investment strategies contributes to the achievement of effective results and, above all, protects the portfolio against a significant loss in the period of strong turmoil on the stock exchange. During the research period, only two strategies (Ichimoku and ETF- Exchange traded fund) would produce negative returns at the worst possible end of the investment. At the best moment, however, the „passive” investment achieved the lowest result. Looking at the final balance at the end of 2019, as many as four systems based on technical analysis were more effective than the „buy and hold” strategy, and at the end of the first quarter of 2020 – all of them. When analyzing the management quality measures, it turned out that taking into account the 21 quarters, the passive strategy had the lowest MAR index. The Sharpe’s measure is also relatively weak compared to the four leading strategies.

scholarly journals The Effectiveness of the Transaction Systems on the Dax Index

2021 ◽  
pp. 159-184
Author(s):  
Marek Trembiński ◽  
Joanna Stawska
Keyword(s):  
Portfolio Management ◽  
Stock Exchange ◽  
Subjective Evaluation ◽  
Technical Analysis ◽  
Quality Measures ◽  
Significant Loss ◽  
Investment Strategies ◽  
Trading Systems ◽  
Management Quality ◽  
Analysis Tools

The purpose of the article/hypothesis: The aim of this article is to examine the effectiveness of trading systems built on the basis of technical analysis tools in 2015–2020 on the DAX stock exchange index. Efficiency is understood as generating positive rates of return, taking into account the risk incurred by the investor, as well as achieving better results than passive strategies. Presenting empirical evidence implying the value of technical analysis is a difficult task not only because of a huge number of instruments used on a daily basis, but also due to their almost unlimited possibility to modify parameters and often subjective evaluation. Methodology: The effectiveness of technical analysis tools was tested using selected investment strategies based on oscillators and indicators following the trend. All transactions were carried out on the Meta Trader 4 platform. The analyzed strategies were comprehensively assessed using the portfolio management quality measures, such as the Sharpe measure or the MAR ratio (Managed Account Ratio). Results of the research: The test results confirmed that the application of described investment strategies contributes to the achievement of effective results and, above all, protects the portfolio against a significant loss in the period of strong turmoil on the stock exchange. During the research period, only two strategies (Ichimoku and ETF- Exchange traded fund) would produce negative returns at the worst possible end of the investment. At the best moment, however, the „passive” investment achieved the lowest result. Looking at the final balance at the end of 2019, as many as four systems based on technical analysis were more effective than the „buy and hold” strategy, and at the end of the first quarter of 2020 – all of them. When analyzing the management quality measures, it turned out that taking into account the 21 quarters, the passive strategy had the lowest MAR index. The Sharpe’s measure is also relatively weak compared to the four leading strategies.

Embedding Technical Analysis Into Neural Network Based Trading Systems

2017 ◽  
pp. 523-541 ◽  
Author(s):  
Tim Chenoweth ◽  
Zoran ObradoviĆ ◽  
Sauchi Stephen Lee
Keyword(s):  
Neural Network ◽  
Technical Analysis ◽  
Trading Systems

scholarly journals Deep Reinforcement Learning Agent for S&P 500 Stock Selection

Axioms ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 130
Author(s):  
Tommi Huotari ◽  
Jyrki Savolainen ◽  
Mikael Collan
Keyword(s):  
Portfolio Management ◽  
Stock Selection ◽  
Real World Data ◽  
Data Set ◽  
Agent Based ◽  
Trading Systems ◽  
Learning Agent ◽  
Trading Agent ◽  
Risk Taker ◽  
Insight Into

This study investigated the performance of a trading agent based on a convolutional neural network model in portfolio management. The results showed that with real-world data the agent could produce relevant trading results, while the agent’s behavior corresponded to that of a high-risk taker. The data used were wide in comparison with earlier reported research and was based on the full set of the S&P 500 stock data for twenty-one years supplemented with selected financial ratios. The results presented are new in terms of the size of the data set used and with regards to the model used. The results provide direction and offer insight into how deep learning methods may be used in constructing automatic trading systems.

scholarly journals TRADER HUB SYSTEM DEVELOPMENT USING VAN K THARP EXPECTANCY THEORY TO ANALYSE RETAIL FOREX TRADING SYSTEM PERFORMANCE

2020 ◽  
Vol 5 (2) ◽  
pp. 523
Author(s):  
Khairul Pakhrudin ◽  
Kamalia Azma Kamaruddin ◽  
Fauziah Ahmad
Keyword(s):  
Foreign Exchange ◽  
System Performance ◽  
System Development ◽  
Technical Analysis ◽  
Expectancy Theory ◽  
Trading System ◽  
Trading Systems ◽  
Systems Quality ◽  
Long Time ◽  
Analysis System

With the advance of technology, foreign exchange trading, known as forex or FX trading, has been conducted electronically using the Internet. Forex traders were using technical analysis to project the best price when buying or selling currencies, and by using the technical analysis tools, they have created their own trading system. Forex traders need to make consistent profitability in the long term to sustain in the forex market, therefore a good trading system is vital. In order to evaluate their trading system performance, forex traders can use the backtesting and forward testing methods. However, these two methods took a long time to perform and did not provide the exact benchmark quality of the trading system. This paper describes how Van K Tharp Expectancy Theory was applied in the development of the Trader Hub System (THS) to evaluate forex trading systems quality. By using the system development life cycle (SDLC) methodology, four phases have been undertaken, which were requirements gathering, requirements analysis, system design, and system development. The outcome is a system that can easily evaluate forex trading system performance; thus, it may help retail forex traders in Malaysia to do technical analysis on their foreign exchange pairs.

scholarly journals CAST: Using neural networks to improve trading systems based on technical analysis by means of the RSI financial indicator

2011 ◽  
Vol 38 (9) ◽  
pp. 11489-11500 ◽  
Author(s):  
Alejandro Rodríguez-González ◽  
Ángel García-Crespo ◽  
Ricardo Colomo-Palacios ◽  
Fernando Guldrís Iglesias ◽  
Juan Miguel Gómez-Berbís
Keyword(s):  
Neural Networks ◽  
Technical Analysis ◽  
Trading Systems ◽  
Financial Indicator

Embedding technical analysis into neural network based trading systems

1996 ◽  
Vol 10 (6) ◽  
pp. 523-542 ◽  
Author(s):  
Tim Chenoweth ◽  
Zoran Obradovic ◽  
Sauchi Stephen Lee
Keyword(s):  
Neural Network ◽  
Technical Analysis ◽  
Trading Systems

Hybrid Automatic Trading Systems: Technical Analysis & Group Method of Data Handling

2002 ◽  
pp. 47-55
Author(s):  
Marco Corazza ◽  
Paolo Vanni ◽  
Umberto Loschi
Keyword(s):  
Technical Analysis ◽  
Group Method ◽  
Data Handling ◽  
Trading Systems ◽  
Analysis Group

scholarly journals Securities Analysis and Portfolio Management of Selected NSE Securities

2021 ◽  
pp. 540-543
Author(s):  
Prof. (Dr) Pramod Sharma
Keyword(s):  
Stock Market ◽  
Portfolio Management ◽  
Intrinsic Value ◽  
Technical Analysis ◽  
Future Price ◽  
Market Participants ◽  
Major Market ◽  
Price Trends ◽  
Indian Stock Market ◽  
A Company

“Technical Analysis is the study of data generated by the action of markets and by the behaviour and psychology of market participants and observers”: -Constitution of the market technicians Association Technical analysis is a completely different approach to stock market investing- it doesn’t try to find the intrinsic value of a company or try to find whether a share is mispriced or undervalued. "Technical analysis is the study of market action, primarily through the use of charts, for the purpose of forecasting future price trends. “A technical analyst is interested only in the price movements in the market. So, it is all about analysing the demand and supply or a price volume analysis. Technical analysis considers only the actual price behaviour of the market or instrument, based on the premise that price reflects all relevant factors before an investor becomes aware of them through other channels. These stock market indicators would help the investor to identify major market turning points. This paper examines the technical analysis of selected companies which helps to understand the price behaviour of the shares, the signals given by them and to assist investment decisions in the Indian stock Market.

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