| 摘要(英) |
Most of the researches related to multi-factor models are aimed at finding effective factor
combinations using various methods, but few studies continue to explore the verification of
multi-factor models combined with technical analysis strategies, and how to build these
automated backtesting and verification platforms. In this regard, this research refers to the
concept of similar experimental design proposed by 謝昀峻 (2018). Based on this, it adds a
multi-factor model combined with a technical analysis strategy dynamic exchange stock
strategy. At the same time, it improves the ability to dynamically allocate capital weights
according to conditions, and use walk forward analysis which is a portfolio optimization method,
proposed by Pardo (2011) to design system process, so let it can dynamically verify the stability
of the strategy. However, its system architecture and process design have various limitations
and cannot be compatible with the above requirements. Therefore, this research uses Python to
develop an automated backtest and verification platform with the above functions. In addition,
because the system process design of the walk forward analysis requires more computing
resources, the system architecture is designed with reference to the multi-node detection task
distributed algorithm used in the research of 林泓志 (2020). It uses multiple hosts to perform
simultaneous operations to share the computational load.
This study uses 8 single-factor and 4 dual-factor combinations with various strategy
combinations to conduct experiments. The main trading strategies include two strategies: buyand-hold and dynamic stock exchange. In addition, dynamic stock exchange strategies
includes other settings, such as in the optimization parameter window configuration, there are
fixed parameters, anchored, and non-anchored; There are equal capital weight and maximum
capital utilization methods in the capital weight distribution method. Besides, we will
experiment with various combinations to select candidate stocks in different groups and the
difference in holding different maximum number of shares. Finally, the performance will be
presented in a variety of visual charts to analyze the performance of the strategy from different
perspectives. |
| 參考文獻 |
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林泓志（2020）。多商品相關性獲利穩定度分析與比較。未出版之碩士論文，國立中央大學資訊管理學系，桃園縣。
蔡永丞（2017）。初始興櫃認購價格的特徵因素與IFRS實施前後的影響分析。未出版之碩士論文，國立中央大學產業經濟研究所在職專班。
鄭皓元（2018）。以分散式運算分析多商品主副策略最適性之自動化平台設計與驗證。未出版之碩士論文，國立中央大學資訊管理學系，桃園縣。
謝昀峻（2018）。運用等分法與核心交易策略於台灣股票之自動化平台設計與實證研究。未出版之碩士論文，國立中央大學資訊管理學系，桃園縣。 |
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