Trading strategies based on a pattern detection algorithm
Sammanfattning: This thesis aims to develop a method to algorithmically detect patterns used in technical analysis. Non-parametric Kernel regression is used to smoothen the otherwise extremely noisy data of how stock prices develop over time. To find these patterns, Previously described quantitatively defined criteria are used with some modifications. In total six patterns are searched for, where three of them are intended to predict an incline of the asset price and three to predict a decline. As a basis for the study, data of 500 U.S stocks are analyzed. These 500 stocks were all present in the S&P500 index at the beginning of 2010 and the daily closing price of each of these assets is obtained from the beginning of 2010 until the end of 2020. This period is divided into two periods, one training set and one test set. The longer training set period is used to optimize trading strategies, and the shorter test period is used to test these strategies. The algorithm to detect these patterns was successfully implemented and this resulted in detection of a sufficient amount of each pattern to be able to evaluate their efficiency during the training period. All of the patterns intended to predict a decline in the asset price failed. This is most likely due to the fact that the stock market has had a nearly continuous increase during the entire study period. These patterns are therefore not used for the analysis of the test period. In contrast, the three remaining patterns, which are all intended to predict an incline of the assets price, could generate excess returns of the risk-free rate, before adjusting for risk. After risk adjustment, two of these patterns outperformed a Buy-and-hold strategy during the training period. The best combinations of parameters for each of these three patterns are then applied on the test data. The most interesting conclusion from the analysis of the test period is that none of the pattern-based strategies that could outperform the Buy-and-hold strategy during the training period can do that during the test period. The conclusion is therefore that the strategies that are able to beat the Buy-and-hold during the training period have a high probability of being over-optimized on that particular data set and do not perform well enough to be relied on.
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