Grid Trading System Robot (GTSbot): A Novel Mathematical Algorithm for Trading FX Market

Grid algorithmic trading has become quite popular among traders because it shows several advantages with respect to similar approaches. Basically, a grid trading strategy is a method that seeks to make profit on the market movements of the underlying financial instrument by positioning buy and sell orders properly time-spaced (grid distance). The main advantage of the grid trading strategy is the financial sustainability of the algorithm because it provides a robust way to mediate losses in financial transactions even though this also means very complicated trades management algorithm. For these reasons, grid trading is certainly one of the best approaches to be used in high frequency trading (HFT) strategies. Due to the high level of unpredictability of the financial markets, many investment funds and institutional traders are opting for the HFT (high frequency trading) systems, which allow them to obtain high performance due to the large number of financial transactions executed in the short-term timeframe. The combination of HFT strategies with the use of machine learning methods for the financial time series forecast, has significantly improved the capability and overall performance of the modern automated trading systems. Taking this into account, the authors propose an automatic HFT grid trading system that operates in the FOREX (foreign exchange) market. The performance of the proposed algorithm together with the reduced drawdown confirmed the effectiveness and robustness of the proposed approach

Advanced Markov-Based Machine Learning Framework for Making Adaptive Trading System

Stock market prediction and trading has attracted the effort of many researchers in several scientific areas because it is a challenging task due to the high complexity of the market. More investors put their effort to the development of a systematic approach, i.e., the so called “Trading System (TS)” for stocks pricing and trend prediction. The introduction of the Trading On-Line (TOL) has significantly improved the overall number of daily transactions on the stock market with the consequent increasing of the market complexity and liquidity. One of the most main consequence of the TOL is the “automatic trading”, i.e., an ad-hoc algorithmic robot able to automatically analyze a lot of financial data with target to open/close several trading operations in such reduced time for increasing the profitability of the trading system. When the number of such automatic operations increase significantly, the trading approach is known as High Frequency Trading (HFT). In this context, recently, the usage of machine learning has improved the robustness of the trading systems including HFT sector. The authors propose an innovative approach based on usage of ad-hoc machine learning approach, starting from historical data analysis, is able to perform careful stock price prediction. The stock price prediction accuracy is further improved by using adaptive correction based on the hypothesis that stock price formation is regulated by Markov stochastic propriety. The validation results applied to such shares and financial instruments confirms the robustness and effectiveness of the proposed automatic trading algorithm