عماد کوشا

Technical analysis is one of the financial market analysis tools. Technical analysis is a method of anticipating prices and markets through studying historical market data. Based on the factors studied in this type of analysis, indicators are designed and presented to facilitate decision-making on buy and sell stress and then buy and sell action in financial markets. This research evaluates performances and returns of 10 conventional technical analysis indicators based on the strategies set on the total stock exchange index, the total index of OTC market and 8 other (non-correlated) industry indices by using Meta Trader software from 2008 to 2018. Also, the significance of the difference between the returns of the indicators is tested using the buy and hold strategy. The results show a significant difference between the returns using some of the technical analysis indicators in some indices and buy and hold strategy. The effectiveness of technical analysis strategies varies across industries and EMA and SMA with respectively 6 and 5 repetitions, are the best strategies and BB with just one repetition has the least repetition. The investment industry index with the most repetition is the industry in which the strategies used in this study have been able to provide an acceptable return.

The purpose of the present study is to use the ensemble learning model to combine the predictions of Random Forest (RF), Long-Short Term Memory (LSTM), and Recurrent Neural Network (RNN) models for the Top and Bottom Prices of Bitcoin. To this aim, in the first stage, Bitcoin's top and bottom prices are predicted using three machine learning models. In the second stage, the outputs of the models are presented as feature variables to the Extreme Gradient Boosting (Xgboost) and Light Gradient Boosting Machine (LightGBM) models to predict the price tops and bottoms. Then, in the third stage, the outputs of the second stage are combined through the voting ensemble classifier pattern to predict the next top and bottom prices. The data of top and bottom Bitcoin prices in the 1-hour time frame from 1/1/2018 to the end of 6/30/2022 are used as target variables and 31 technical analysis indicators as feature variables for the three models in the first stage. 70% of the data is regarded as learning data, and the remaining 30% is considered for the second and third stages. In the second phase, 50% of the data is considered for learning the output of the previous stage and 50% for the test data. Finally, the prediction values are evaluated with real data for the three models and the proposed ensemble learning model. The results reveal the improvement of the performance, precision, and accuracy of the ensemble model compared to weak learning models.