摘要(英) |
Global financial markets are highly connected. There exist different relationships between different markets. It is a big issue to find out lots of multiple financial products correlation efficiently and analyze lots of financial product pairs on profit robustness within in-sample and out-of-sample. However, there is no a well-built system that could solve this problem and achieve this goal.
The purpose of this research is to find out the Intermarket correlation of multiple products efficiently. This study is based on the strategy which Ruggiero(2012)proposes to develop, design, and implement a platform that could automate calculation, backtest, and analysis multiple financial product correlation. This high flexibility platform allows users to customize their own backtest tasks including financial products, periods splits, parameters, and the other configurations. To rank and analysis different pairs the robustness of profit, using the risk parity concept to standardize different kinds of financial products. It could be divided into two parts in-sample and out-of-sample at the stage of the analysis. In-sample applies the “Plateau Searching Algorithm” to find out the sound and robust optimization result surface and to prevent the optimization from overfitting. Based on the prior results, using the DBSCAN to analyze and develop “Plateau Indicator” to evaluate the robustness between different optimization surfaces. Out-of-sample uses the daily profit data to judge the existence parameters correlation to rank the pairs and verification the effectiveness of pairs. Besides the Ruggiero(2012) strategy, other strategies could also practice the architecture of this study to analyze and verification the effectiveness of pairs of correlation.
In this study, using 122 financial products in the American market, from January 1, 2002, to August 31, 2019, are used to analyze and verify this platform. Among these financial products, 47 financial products could be used for trading, the others 122 could be used to confirm the relationship, in summary, produces 5,687 combinations. All of the combinations would apply to different time splits, optimization points threshold, demand of average return, and rank methods to analyze the different pairs performance and the level of robustness on profit. In order to analysis on multiple financial products would be consumed lots of time and computing resources to generate the performance files and needed data, using the “Host-Node-Message Detection Distribution Algorithm” to integrate multiple computers to accelerate the efficiency of computing process.
Finally, the result of this platform analysis would be presented by the charts and tables of the data visualization. This could help the user to perceive the correlation and robustness between different financial products and realize the level of different market correlations quickly. It would be help users to determine the investment targets. |
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