In the last decade, High-Frequency Trading (HFT) has become a popular issue in the futures market, which has attracted much attention from numerous researchers. In this study, an intelligent decision support system is proposed for apple futures high-frequency trading. First, three eXtreme Gradient Boosting (XGBoost) based models use the feature inputs from multiple time scales for return and direction prediction. Then, based on a pre-designed trading rule, the signals of long and short-selling are determined, and corresponding transactions are executed. In order to retain considerable profits in time and to avoid serious losses possibly caused by sudden and huge price changes toward the opposite direction as predictions, a position closing function is implemented in the trading rule. Meanwhile, Particle Swarm Optimization (PSO) is employed to optimize the parameters of the trading rule as well as the XGBoost parameters. By evaluating the experimental results, we observed that the proposed approach successfully achieved the best performance in terms of direction prediction accuracy, transaction returns, as well as return/risk ratio. It could be inferred from the experimental results that the proposed approach could provide decision support and beneficial reference for market traders involved in high-frequency trading of the apple futures.

/pdf/a-decision-support-system-for-trading-in-apple-futures-3ie5ppujhg.pdf

A Decision Support System for Trading in Apple Futures Market Using Predictions Fusion

A novel hybrid method for direction forecasting and trading of Apple Futures

For this research, we implemented a trading system based on the Turtle rules and examined its efficiency when trading selected assets from the Forex, Metals, Commodities, Energy and Cryptocurrency Markets using historical data. Afterwards, we enhanced our Turtle-based trading system with additional conditions for opening a new position. Specifically, we added an exclusion zone based on the ATR indicator, in order to have controlled conditions for opening a new position after a stop loss signal was triggered. Thus, AdTurtle was developed, a Turtle trading system with advanced algorithms of opening and closing positions. To the best of our knowledge, for the first time this variation of the Turtle trading system has been developed and examined.

AdTurtle: An Advanced Turtle Trading System

Intuitionistic fuzzy rule-base evidential reasoning with application to the currency trading system on the Forex market

Given the popularity and propagation of automated trading systems in financial markets among institutional and individual traders in recent decades, this work attempts to compare and evaluate such ten systems based on different popular technical indicators in combination – for the first time – with the d-Backtest PS method for parameter selection. The systems use the technical indicators of Moving Averages (MA), Average Directional Index (ADX), Ichimoku Kinko Hyo, Moving Average Convergence/Divergence (MACD), Parabolic Stop and Reverse (SAR), Pivot, Turtle and Bollinger Bands (BB), and are enhanced by Stop Loss Strategies based on the Average True Range (ATR) indicator. Improvements in the speed of the back-testing computations used by the d-Backtest PS method over weekly intervals allowed examining all systems on a 3.5 years trading period for 7 assets in financial markets, namely EUR/USD, GBP/USD, USD/JPY, USD/CHF, XAU/USD, WTI, and BTC/USD. To evaluate the systems more holistically, a weighted metric is introduced and examined, which, apart from profit, takes into account more factors after normalization like the Sharpe Ratio, the Maximum Drawdown and the Expected Payoff, as well as a newly introduced Extended Profit Margin factor. Among the automated systems examined and evaluated using the weighted metric, the Adaptive Double Moving Average (Ad2MA) system stands out, followed by the Adaptive Pivot (AdPivot), and the Adaptive Average Directional Index (AdADX) systems.\nAcknowledgmentsWe would like to thank Dr. Christos Schinas for his time and invaluable guidance towards the methodology of the weighted metric. We would also like to thank Michalis Foulos for the hardware setup and support and Nektarios Mitakidis for his contribution to the representation of the results.This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-02342).

/pdf/automated-trading-systems-evaluation-using-d-backtest-ps-1z4neayor3.pdf

Automated trading systems’ evaluation using d-Backtest PS method and WM ranking in financial markets

Back testing process is widely used today in forecasting experiments tests. This method is to calculate the profitability of a trading system, applied to specific past period. The data which are used, correspond to that specific past period and are called “historical data” or “training data”. There is a plethora of trading systems, which include technical indicators, trend following indicators, oscillators, control indicators of price level, etc. It is common nowadays for calculations of technical indicator values to be used along with the prices of securities or shares, as training data in fuzzy, hybrid and support vector machine/regression (SVM/SVR) systems. Whether the data are used in fuzzy systems, or for SVM and SVR systems training, the historical data period selection on most occasions is devoid of validation (In this research we designate historical data as training data). We substantiate that such an expert trading system, has a profitability edge—with regard to future transactions—over currently applied trading strategies that merely implement parameters’ optimization. Thus not profitable trading systems can be turned into profitable. To that end, first and foremost, an optimal historical data period must be determined, secondarily a parameters optimization computation must be completed and finally the right conditions of parameters must be applied for optimal parameters’ selection. In this new approach, we develop an integrated dynamic computation algorithm, called the “d-BackTest PS Method”, for selection of optimal historical data period, periodically. In addition, we test conditions of parameters and values via back-testing, using multi agent technology, integrated in an automated trading expert system based on Moving Average Convergence Divergence (MACD) technical indicator. This dynamic computation algorithm can be used in Technical indicators, Fuzzy, SVR and SVM and hybrid forecasting systems. The outcome crystalizes in an autonomous intelligent trading system.

Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting

State-of-the-art trading systems are automated and are executed on computers through trading platforms. They generate and execute trades, based on optimized parameters and algorithmic trading strategies. In the current research, such software for automated trading systems was developed, utilizing the following technical indicators, the MACD (oscillator), the SMA (moving average) and the PIVOT points (price crossover).The systems traded on hourly timeframes, using historical data of closing prices over weekly based periods of parameters' optimization and using the dBacktest PS method. Through this research, and the interpretation and evaluation of results, two findings or rather conclusions were drawn. These findings are presented sequentially as follows: In terms of profitability, the adaptive MACD trading system was the most effective one, followed by PIVOT trading system and the SMA was ranked as the least profitable trading system. There is a weak correlation of back testing periods among the above trading systems.

Performance Comparison of Three Automated Trading Systems (MACD, PIVOT and SMA) by Means of the d-Backtest PS Implementation

Trading strategies intended for high frequency trading in Forex markets are executed by cutting-edge automated trading systems. Such systems implement algorithmic trading strategies and are configured with predefined optimized parameters in order to generate entry and exit orders and execute trades on trading platforms. Three high-frequency automated trading systems were developed in the current research, using the MACD (oscillator), the SMA (moving average) and the PIVOT points (price crossover) technical indicators. The systems traded on hourly time frames, employing historical data of closing prices and the parameter optimization for each system was done using the d-Backtest PS method over weekly periods. With this work we intend to extend the methods of parameter selection for automated trading systems in high frequency trading. Through this research and the interpretation and evaluation of its results, we conclude that backtesting parameters’ optimization, especially through the d-Backtest PS method, is much more profitable than the default values of the parameters and that the optimization of parameters yields the highest profits through the implementation of restrictive relationships among them. It is also observed that the selection of the most profitable parameters of a trading system can be unrestricted, rendering the validation of the minor divergence occurring among slightly varying prices redundant. Meanwhile, other conclusions that can be drawn are that the most profitable classification system employed by the d-Backtest PS method is calibrated by means of two validation periods and that the most efficient profitability ratio between historical data period and validation period is 6:1 (in- and out-of-the-sample ratio).

Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method

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Papers

Profitability Edge by Dynamic Back Testing Optimal Period Selection for Technical Parameters Optimization, in Trading Systems with Forecasting

Performance Comparison of Three Automated Trading Systems (MACD, PIVOT and SMA) by Means of the d-Backtest PS Implementation

Optimization of Backtesting Techniques in Automated High Frequency Trading Systems Using the d-Backtest PS Method