File Name: filter rules and stock market trading .zip
- Filter Rule
- Filter Rules and Stock-Market Trading
- Algorithmic trading
- Stock Market Trading Rules Discovery Based on Biclustering Method
Abstract Finding the best trading rules is a well-known problem in the field of technical analysis of stock markets. However, depending on the problem size, their application might not be a viable option as the iterative search through a multitude of possible solutions does take considerable time.
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The purpose of this paper is to examine the predictability of nine filter rules and test the validity of the weak form of the efficient market hypothesis for the Qatar Stock Exchange QSE. This study adopts the filter rule strategy employed by Fifield et al. This strategy recommends that the share is held until its price declines by X percent from the subsequent high price. Any price changes below X percent are ignored. Additionally, using the theory of weak-form efficiency, this paper suggests that, if a stock market is efficient, an investor cannot achieve superior results by using these trading rules.
Filter Rules and Stock-Market Trading
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Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. In the twenty-first century, algorithmic trading has been gaining traction with both retail and institutional traders. The term algorithmic trading is often used synonymously with automated trading system.
Stock Market Trading Rules Discovery Based on Biclustering Method
In this paper, a biclustering algorithm is introduced to find the local patterns in the quantized historical data. The local patterns obtained are regarded as the trading rules. Then the trading rules are applied in the short term prediction of the stock price, combined with the minimum-error-rate classification of the Bayes decision theory under the assumption of multivariate normal probability model. In addition, this paper also makes use of the idea of the stream mining to weaken the impact of historical data on the model and update the trading rules dynamically. The experiment is implemented on real datasets and the results prove the effectiveness of the proposed algorithm.
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We need to install standard packages and custom packages. After loading all the packages, we initialize are variables and then download data. Then we need to get the data and define initial variables. Download using getSybmols. Indicator is just a function based on price.
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