| dc.description.abstract | In the study of Hao-Yuan Zheng (2018), a platform was established to automate the analysis and evaluation of a single trading strategy. The platform has the ability to automate operations for a single strategy and multi-portfolio, and the computational process can reduce computation time through multi-computer decentralized operations and present analysis results based on visual charts.
However, the platform does not have a formal comparison model and framework for the computational analysis of multi-strategy and multi-portfolio, and the performance of the filter combination in multi-strategy is not mentioned. There is also a structured design in which the relay performance files generated during the calculation process are not uniformly stored and managed, and it is not easy to be attenuated in the way of amplifying the visual analysis charts for multi-strategy analysis.
Therefore, this study moves the automated trading platform to an easy-to-add interactive platform (jupyter) for strategy calculation and analysis of the chart, so that the object analysis can be used to expand the multi-analysis chart. Auxiliary presentation of single strategy or multi-strategy analysis results, in order to facilitate subsequent comparisons, can also derive the benefits of the filter combination. Finally, through the module, the performance files distributed to each computer are centralized and redistributed to achieve the purpose of unified management of performance files.
In the study, after the original single trading strategy (Bolling Band), two additional trading strategies (Moving Average strategy & Keltner Channel) were added, and 1690 items of stocks were used as data sets to verify whether the platform can carry out the operational analysis of multi-portfolio and multi-strategy. At the same time of verification, we can also compare the advantages and disadvantages of the trading strategy, and find out which filter combinations can be applied to multiple trading strategies, and also find out which portfolio can be stably profited from the data produced by the platform. .
This study found that Keltner Channel′s trading strategy has the characteristics of “high profitability and high risk” for multi-portfolio, and is the best performing strategy among the three trading strategies. In the aspect of the filter combination, the combination of ＂the transaction volume is less than 10 million, the transaction volume is more than 2 to 15 times the average of the previous five days, and the stock price change rate is less than 0.5% to 1%.＂ is the best performance in the combination. The study also found that 284 portfolio have good profitability and can be stable in these three trading strategies.
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