金融科技領域近年來蓬勃發展，在金融商品投資的環節，隨著個人電腦與網路的普及，金融資料的獲取越來越容易，基於資料與演算法的算法交易投資策略也越發流行。這意味著投資者不再只是仰賴專業的金融機構提供投資建議，而是選擇自己喜愛的算法交易策略進行自動化的投資，更可以透過程式或是算法交易開發平台設計自己的交易策略，投資的主動權回到了大眾的手上。然而，策略模型有不同的設計緣由與績效的評估方法，投資者不易了解模型參數的意義與影響，也難以將不同的模型比較，選出符合他偏好的投資策略。
針對此問題，本研究將基於算法交易的回測流程，設計整合異質性交易策略的流程元模型，投資者透過此元模型將可以把不同交易策略的績效一致化，達到使不同的交易策略可以一致的比較。同時，本研究也將使用此元模型設計視覺化的績效比較平台，此平台可以匯入不同的交易策略模型，藉由網頁表單調整模型參數，呼叫模型進行回測，並將模型回測的結果一致化的儲存，最終將不同模型的績效使用折線圖、長條圖等互動圖表疊圖顯示，投資者將可以輕易地分析模型的績效差異。本研究還設計樞紐分析與參數績效的篩選器，進一步提供模型的分析流程，投資者能從流程中對該模型的參數與績效有更多的理解，將能從本平台找到符合他偏好的投資策略。
未來在本研究的平台能擴增更多的交易模型，並加上更多不同的績效評估指標，從更多的面向分析比較不同交易模型的差異，讓投資者可以快速且精確地找到他所偏好的模型。

In recent years, fintech has risen rapidly. In the financial investment, with the popularization of personal computers and the Internet, it is easier to obtain financial data, and investment strategies based on data and algorithms are becoming more and more popular.
This means that investors no longer rely on professional financial institutions to provide investment advice, but choose their favorite algorithmic trading strategies for algorithmic trading, and can design their own trading strategies through programs or algorithmic trading development platforms. Thus, they become empowered investors.
However, each strategy model has different design reasons and performance evaluation metrics. It is difficult for investors to understand the meaning and effects of model parameters, and it is also difficult for investors to compare different models and choose investment strategies that meet their preferences.
In response to this problem, this study will design a meta-process model that integrates different trading strategies based on the back-testing process of algorithmic trading. Through this meta-process model, investors will be able to align the performance of different trading strategies, so that different trading strategies is comparable.
At the same time, this research will also use this meta-process model to design a visual performance comparison platform. This platform can import different trading strategy models, adjust model parameters through web forms, then call the model for back-testing, and report the results of the back-testing. The performance of different models is finally displayed using interactive chart overlays such as line charts and bar charts, thus investors will be able to easily analyze the performance of different models.
This research also provides pivot analysis and a filter for parameters and performance metrices. Furthermore, it provides the suggested analysis process of the model. Investors can have a better understanding of the parameters and performance of the model from the process, and will be able to find out what model suits their preferences from this platform.
In the future, this research platform can expand more trading models, and add more performance evaluation metrics. Comparing trading models from different perspective, so that investors can quickly and accurately find their preferred strategy.

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