Algorithmic Trading

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 tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.

One of the important factors in making a profit through financial markets is a quick and correct response to market events, which is possible only by examining all aspects of the market. Today, to solve this challenge, the use of trading algo-rithms has become inevitable and can be considered as transactions made by computers that these transactions are controlled and reviewed through algorithms. Depending on their type and purpose, these algorithms examine different aspects and, according to the strategies defined for them, make decisions and signal by order registration. These trading methods are growing rapidly in the world, espe-cially in strong and developed financial markets. Proper implementation of algo-rithmic transactions reduces transaction costs and increases the accuracy of inves-tors in their investments. One of the most widely used of these strategies is the trend-following strategy, which is welcomed by many traders. This strategy can be implemented in different ways and through different trading tools. In the pre-sent study, five types of them were examined and implemented on one of the most traded symbols of the Tehran Stock Exchange. The purpose of this study is to implement some of the popular strategies in algorithmic trading along with the introduction of algorithmic trading, its strategies in the Iranian stock market, which includes the study of its advantages and disadvantages. The present study is a cross-sectional retrospective and field survey in terms of applied purpose and in terms of data collection.

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.

Recent studies on financial markets have demonstrated that technical analysis can help us effectively predict the stock market index trend. Business systems are widely used for stock market analysis. This paper uses a genetic algorithm (GA) to develop a stock market trading optimization system. Our proposed system can generate a decision-making strategy for buying, holding, and selling stocks for each day and generate high returns for each stock. The system consists of two stages: removing restricted stocks and producing a stock trading strategy. Accordingly, evolutionary computation, like GA, is highly promising because of its intelligence, flexibility, and search strength (fast and efficient). The multiple-objective nature of the utilized algorithm can be regarded as the center of gravity of the research question. The proper functioning or malfunctioning of the resulting portfolio management can be employed as a benchmark for selecting or discarding the algorithm. On the other hand, the research question is focused on the application of technical analysis indicators. Therefore, both aspects of the research question, namely the multiple-objective nature of the algorithm in terms of the analysis method and technical indicators in terms of features selected for analysis, must be taken into account.