Do you want to use a trend-following strategy or a counter-trend strategy?.Should your system take into account one condition or multiple conditions?.Do you want to take into account high and low prices in your buying and selling decisions?.What are your favorite indicators among the numerous ones available to you?.Do you have a trading method you saw in a book or on the internet that you would like to test?.Your replies to the following questions may give you an initial idea of the type of strategy you want to create: You can then easily decide whether or not you want to continue with this idea and later improve it based on your experience. ProRealTime then allows you to test these buying and selling conditions before deciding to actually use them. In conclusion, the proposed system is very effective and encouraging in that it predicts the future trading points of a specific stock.1 The initial idea Determine the conditions to buy / sellįrom an "initial idea", you will define the buying and selling conditions of your strategy. The experimental results show that the IPLR approach can make significant amounts of profit on stocks with different variations. The proposed system is tested on three different types of stocks, i.e., uptrend, steady, and downtrend. Thus, it further increases the profitability of the model. An intelligent PLR (IPLR) model is further developed by integrating the genetic algorithm with the PLR to iteratively improve the threshold value of the PLR. After this, a new set of test data can trigger the model when a buy or sell point is detected by BPN. As a result, temporary turning points (trough or peak) of the historical stock data can be detected and inputted to the backpropagation neural network (BPN) for supervised training of the model. In this paper, a different approach is taken by applying PLR to decompose historical data into different segments. As such, similar patterns can be retrieved from historical data and future prices of the stock can be predicted according to the patterns retrieved. Recently, the piecewise linear representation (PLR) method has been applied to the stock market for pattern matching. In future work different stock/ETF data and different combined options strategies will be included in the model to identify performances of different techniques. The results indicate that the proposed model outperformed not only the buy and hold strategy, but also buying and selling the ETF alone without the options. The system is trained using the data between 20 the testing is done with 2009 data. Historical end-of-day close values and options data for the S&P 500 Spider ETF (SPY) between years 2005-2009 are used. In this particular study, genetic algorithms (GA) and Particle Swarm Optimization (PSO) are chosen as the soft computing methods for optimization. Again, the optimization is implemented with evolutionary computation. Then using the selected parameters, in the second level actual trading is performed using an optimized covered call strategy. In the first level, the buy/sell signals are created by optimizing the RSI technical indicator parameters with evolutionary computation techniques. In this study, a two-level cascade stock trading model is proposed.
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