This work presents the complexity that emerges in a Bertrand duopoly between two companies in the Greek oil market, one of which is semi-public and the other is private. The game uses linear demand functions for differentiated products from the existing literature and asymmetric cost functions that arose after approaches using the published financial reports of the two oil companies (Hellenic Petroleum and Motor Oil). The game is based on the assumption of homogeneous players who are characterized by bounded rationality and follow an adjustment mechanism. The players’ decisions for each time period are expressed by two difference equations. A dynamical analysis of the game’s discrete dynamical system is made by finding the equilibrium positions and studying their stability.

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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.

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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.

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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.

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Modern trading systems are mechanic, run automatically on computers inside trading platforms and decide their position against the market through optimized parameters and algorithmic strategies. These systems now, in most cases, comprise high frequency traders, especially in the Forex market.
In this research, a piece of software of an automatic high frequency trading system was developed, based on the technical indicator PIVOT (price level breakthrough). The system made transactions on hourly closing prices with weekly parameters optimization period, using the d-Backtest PS method.
Through the search and checking of the results, two findings for optimization of trading strategy were found. These findings with the order they were examined and are presented in this paper are as follows: (1) the simultaneous use of “long and short” positions, with different parameters in a hedging account, acts as a hedging strategy, minimizing losses, in relation to a “long or short” in a non-hedging account for the same time period and (2) there is weak correlation of past backtesting periods between the same systems, if they are configured for “long and short” trades, or for just “long” or for just “short”.

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A lot of strategies for Take Profit and Stop Loss functionalities have been propounded and scrutinized over the years. In this paper, we examine various strategies added to a simple MACD automated trading system and used on selected assets from Forex, Metals, Energy, and Cryptocurrencies categories and afterwards, we compare and contrast their results. We conclude that Take Profit strategies based on faster take profit signals on MACD are not better than a simple MACD strategy and of the different Stop Loss strategies based on ATR, the sliding and variable ATR window has the best results for a period of 12 and a multiplier of 6. For the first time, to the best of our knowledge, we implement a combination of an adaptive MACD Expert Advisor that uses back-tested optimized parameters per asset with price levels defined by the ATR indicator, used to set limits for Stop Loss.

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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 d- Backtest 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.

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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.

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Η εφαρμογή της τεχνολογίας τεχνητής νοημοσύνης στην ανάλυση των χρηματοοικονομικών αγορών έχει λάβει σημαντική προσοχή λόγω της ανάπτυξης της τεχνολογίας επεξεργασίας πληροφοριών, ιδίως της βαθιάς μάθησης. Αυτό το ειδικό τεύχος είναι μια συλλογή από 10 άρθρα σχετικά με το «AI και Financial Markets» και περιέχει τέσσερα άρθρα σχετικά με τη μηχανική εκμάθηση, δύο άρθρα που βασίζονται σε τεχνητή προσομοίωση αγοράς και, τέσσερα άρθρα σχετικά με την εφαρμογή άλλων προσεγγίσεων και ένα άρθρο έννοιας.

Όταν διαπραγματεύονται ένα περιουσιακό στοιχείο, οι επενδυτές εκτίθενται σε δυνητικά υψηλό κίνδυνο εάν η τιμή μετακινηθεί προς μια κατεύθυνση που είναι αντίθετη από αυτήν που περίμεναν. Αυτό θα μπορούσε να οδηγήσει σε σημαντικές απώλειες στο επενδυτικό κεφάλαιο, εκτός εάν ληφθούν άμεσα μέτρα για έξοδο από το μη κερδοφόρα θέση το συντομότερο δυνατό. Από την άλλη πλευρά, εάν η τιμή κινείται προς μια κατεύθυνση που κάνει την τρέχουσα θέση κερδοφόρα, ένας επενδυτής μπορεί να θέλει να κλείσει τη θέση και να εξαργυρώσει τα κέρδη που αποκτήθηκαν μέχρι εκείνο το σημείο, καθώς υπάρχει πάντα η πιθανότητα οι κερδοφόρες συναλλαγές να μετατραπούν σε χαμένες θέσεις και να οδηγήσουν σε καταστροφικές απώλειες.

Υπάρχουν τρία επίπεδα μελέτης του εγχειριδίου αυτού. Για τους αρχάριους καλό θα ήταν να αρχίσει η ανάγνωση απ’ την πρώτη κιόλας σελίδα έτσι ώστε να υπάρξει η σχετική ενημέρωση, στα ουσιαστικά, συχνής χρήσης, δεδομένα προγραμματισμού. Όσοι πάλι είχαν κάποτε επαφή με την γλώσσα, ας φρεσκάρουν τη μνήμη τους αρχίζοντας από το πρώτο μέρος. Ο πεπειραμένος σε Visual Basic χρήστης μπορεί κάλλιστα να κάνει μια γρήγορη ανάγνωση το δεύτερο μέρος και να δει τα νέα στοιχεία της έκδοσης 4.0. Ο αναγνώστης ο οποίος ενδιαφέρεται για συγκεκριμένο τομέα ανάπτυξης εφαρμογών, μπορεί να δει το θέμα μέσα από τα κεφάλαια που τον καλύπτουν. Και στις τρεις αυτές φόρμες μελέτης, χρειάζεται απαραίτητα η εμπέδωση του κεφαλαίου IV με τίτλο «Προγραμματισμός κάτω από Windows». Με το αντικείμενο αυτό θα μπορέσετε να εκμεταλλευτείτε σχεδόν όλες τις δυνατότητες που προσφέρει η Visual Basic και το λειτουργικό σας σύστημα.