Strategy backtesting which is a process to take some user-defined trading rules and market information as input, simulate behaviors of market participants under these trading rules and finally report the strategy performance. Strategy backtesting systems are the most important applications of algorithmic trading since it provide individual or professional traders flexibility to design and test their customized ideas. Technical analysis is the most popular way for financial instruments traders to predict future price movement by researching historical data records. However, technical analyses highly rely on a various of technical indicators, patterns and personal charting heuristics, which makes it very difficult to be transformed into algorithmic trading strategies. Most technica...[ Read more ]

Strategy backtesting which is a process to take some user-defined trading rules and market information as input, simulate behaviors of market participants under these trading rules and finally report the strategy performance. Strategy backtesting systems are the most important applications of algorithmic trading since it provide individual or professional traders flexibility to design and test their customized ideas. Technical analysis is the most popular way for financial instruments traders to predict future price movement by researching historical data records. However, technical analyses highly rely on a various of technical indicators, patterns and personal charting heuristics, which makes it very difficult to be transformed into algorithmic trading strategies. Most technical traders especially for individual traders, on the other hand, are lack of knowledge about computer programming, and find it hard for them to script their strategies by coding. Therefore, a strategy backtesting system allowing users to describe complex and advanced technical strategies via pure graphical user interface (GUI) is in very highly demand.

In this thesis, we design and implement a multi-time-frame technical pattern and strategy backtesting system, that is MTPS, to address the problem of describing complex technical patterns on GUI and applying these patterns in algorithmic trading strategies. By observing the cases that a large amount of profitable technical patterns are composed of sequences of characteristic candle bars, we introduce a hierarchical MTPS strategy description system that promote users to describe their technical patterns by describing sequences of technical events. This strategy description system endow users who are not knowledgeable about computer programming a much higher flexibility to include complicated and advanced patterns in their algorithmic strategies. In addition, we also provide users interfaces in MTPS to include technical indicators from multiple time-frames in one strategy which is a unique feature that most of the state-of-the-art strategy backtesting systems can not achieve. Besides, we present present the complete architecture of MTPS including the implementation details of all the software components and how they are organized in an event-driven design pattern. Moreover, we propose an efficient pattern matching algorithm for MTPS to minimize the delay between market data arrival and order placement. Last but not least, experiments are conducted showing that MTPS do capable to describe 3 complex and advanced technical pattens and backtesting strategies based on these 3 patterns to generate profit surpassing the ”buy-and-hold” strategies.