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Low risk trading algorithm based on the price cyclicality function for capital markets

Authors:
  • Algorithm Invest

Abstract and Figures

Buy cheap and sell more expensive is one of the basic ideas of trading the capital markets for hundreds of years. To apply it in practice has become difficult nowadays due to the high price volatility. The uncertainty in the price movements often leads to high-risk allocation. One main question is when the price is low enough for a low-risk entry? Once established an entry point, the second question is how long to keep the open trades in order to optimize the investment efficiency? This article will answer these questions. A general trading algorithm based on the price cyclical behavior will be revealed. The mathematical model is developed using the Price Cyclicality Function combined with other computational techniques in order to establish low-risk intervals. The algorithm will use multiple entry points in order to catch the best price opportunities. A simple empirical exit algorithm will be optimized in order to maximize the profit for a certain capital exposure level. The presented model uses a low number of functional parameters which can be optimized with a reasonable computational effort for any financial market. Trading results obtained for several markets will also be included in this paper in order to reveal the efficiency of the presented methodology. It was found that the Low-Risk Trading Algorithm can be used with good results for algorithmic trading in any financial market. With the right parameters set, this methodology can be wide range applied in the stock markets, currency and cryptocurrency markets, commodities and raw materials markets and even for the real estate investments. The simplicity of the presented model and the good efficiency level obtained will recommend it. This methodology can be used by any investor in order to manage the investment plan with multiple capital markets.
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... For the currency and commodity markets, reliable trading models that can be automated can be found in [8], [9], and [10]. Original investment models and methodologies especially optimized for automated capital investment software can be found in [11], [12], [13], [14], [15], and [16]. Essential psychological strategies that can be used for better adaptation to the context of actual markets can be found in [17], [18], and [19]. ...
... Even the TPL exceeded in values the PPL, because of a strong descending PCY function behavior, this was not a buy signal. All of these considerations can be automated using the formula: (14) where ξ is the limit of PCY function, having the purpose to filtering the false signals. The parameter ξ will be optimized using numerical methods for each market and for each timeframe used. ...
... The parameter ξ will be optimized using numerical methods for each market and for each timeframe used. In Formula (14), k is also a functional parameter. Usually, it can be used as k=1, k=2, or k=3 in order to catch the signals in different volatility market conditions. ...
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ABSTRACT Algorithmic capital investment procedures became the essential tools to make a profit in the volatile price markets of the 21st century. A large number of market participants, private traders, companies, or investment funds are buying and selling on thousands of markets every day to make a profit. After the 2010 year, algorithmic trading systems became a significant part of the capital investment environment. The price evolution is analyzed today in real-time by powerful computers. To buy cheap and to sell more expensive is a simple idea, but to put it on practice is not easy today in very volatile price markets. The orders are built and set almost instantly today by artificial intelligence software using special mathematical algorithms. These procedures automatically decide the best moments to buy and to sell on different financial markets depending on the price real-time movements. This paper will present a specific methodology to analyze the time price series of any capital market. The model will build reliable trading signals to enter and to exit the market to make a profit. The presented method uses trigonometric interpolation of the price evolution to build a significant trend line called here the Trigonometric Trend Line. It will be mathematically proved that this function is in a positive and direct correlation with the price evolution. The Trigonometric Trend Line will be used to build and automate capital investment signals. Besides, the introduced function will be used in order to qualify the actual price trend and to measure the trend power in order to decide if the price makes an important evolution or not. Limit conditions will be imposed in the financial market to avoid trading in non-significant price movement and to reduce the risk and capital exposure. Comparative trading results obtained with the presented methodology will be included in the last part of this paper to qualify the model. Each trading signal type presented in the paper was traded separately to have a qualitative image. Also, all capital trading signals built with the Trigonometric Trend Line were traded together in order to obtain a better risk to reward ratio. To classify the presented methodology, the presented results were compared with real trading profits obtained with the other three well-known capital investment strategies. With all of these, it was found that using the Trigonometric Trend Line reliable automated trading procedures can 7 th International Symposium "SOCIO-ECONOMIC ECOSYSTEMS " January 22-24, 2020 University of Alicante , Spain Please send to: abstract-submission@bslab-symposium.net be made and optimized for each financial market to obtain good results in the capital investment. Being exclusively a mathematical model, the Trigonometric Trend Line methodology presented in this paper can be applied with good results for any algorithmic trading and high-frequency trading software. The functional parameters can be optimized for each capital market and for each timeframe used in order to optimize the capital efficiency and to reduce the risk. The optimization methods will use the historical time price series in order to catch the price behavior and specificity of each market. The reduced number of parameters and the simplicity of the presented method recommend the Trigonometric Trend Line model to be used in any advanced algorithmic trading software.
... The parameter φ is the maximal distance of the price from the PPL function where the price is considered high enough in order to wait the next local minimum point. The limit conditions (14) can be used with good results to automate the exit trading decisions for buy trades made with any other trading strategy both for algorithmic trading and high-frequency trading procedures. For those market cases in which sell trades can be considered, the limit conditions in order to exit the short trades can be expressed with: ...
... The limit conditions imposed by (14) and (15) on daily or four-hour time frames can also be combined with the limit conditions presented in [17], [11], and [12] imposed in a smaller time frame as one hour or even lower, in order to filter all overbought and oversold trades. ...
... The trading signals in Table I were assembled into four-hour time frame interval. The results presented in Table I are obtained using (11) for δ=10 and (14) for φ=100. In Fig. 9 is represented the capital variation for the trading case with (9) without the limit entry conditions and the same variation for the case for (11) using the limit conditions. ...
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In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.
... The parameter φ is the maximal distance of the price from the PPL function where the price is considered high enough in order to wait the next local minimum point. The limit conditions (14) can be used with good results to automate the exit trading decisions for buy trades made with any other trading strategy both for algorithmic trading and high-frequency trading procedures. For those market cases in which sell trades can be considered, the limit conditions in order to exit the short trades can be expressed with: ...
... The limit conditions imposed by (14) and (15) on daily or four-hour time frames can also be combined with the limit conditions presented in [17], [11], and [12] imposed in a smaller time frame as one hour or even lower, in order to filter all overbought and oversold trades. ...
... The trading signals in Table I were assembled into four-hour time frame interval. The results presented in Table I are obtained using (11) for δ=10 and (14) for φ=100. In Fig. 9 is represented the capital variation for the trading case with (9) without the limit entry conditions and the same variation for the case for (11) using the limit conditions. ...
Article
Full-text available
In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.
... For instance, highpressure collection techniques might deter potential customers and lead to lost sales. Keeping inventory too low risks stock-outs, not being able to respond to sales peaks or special customer needs, and a bad reputation among customers (see foe example Pauna, 2019). Further, a firm cannot delay payments indefinitely, because it would risk good relationships with suppliers, creditworthiness (restricted access to bank credits) and legal consequences ( Van der Wielen et al., 2006). ...
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This study investigates empirically the development of working capital management and its impact on profitability and shareholder value in Germany. We analyse panel data of 115 firms listed on the German Prime Standard, covering the period from 2011 to 2017. The results provide evidence that efficient working capital management, indicated by a shorter cash conversion cycle, deteriorated over time, but that a shorter cash conversion has a positive impact on profitability and shareholder value. The findings highlight the need that managers should give greater priority to working capital optimization, even in a low-interest environment. The paper contributes to the literature by advancing this research area in Germany, and it is the first study investigating shareholder relationship with working capital management and all its determinants.
... A particular approach for risk management techniques that can be used in automated decision-making systems for capital investments can be found in (Vince, 1992). Mathematical models especially designed and optimized for algorithmic trading with proved and sustained results in real capital investments can be found in (Păuna & Lungu, 2018), (Păuna, 2018a), (Păuna, 2018b), (Păuna, 2018c), (Păuna, 2019a), (Păuna, 2019b), (Păuna, 2019c), (Păuna, 2019d), (Păuna, 2019e), (Păuna, 2019f), and (Păuna, 2020). ...
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Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers using advanced mathematical algorithms. This paper will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments.
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After several attempts to publish my Ph.D. thesis with different prestigious publishers, I have decided to make this work public and free of charge for anyone. Enjoy! Cristian Păuna
Chapter
Capital investment is a sustained activity nowadays. After the worldwide release of the electronic trading systems, automated decision-making investment software is the new trend in financial speculation. A significant part of capital trading is fully computerized today. The buying and selling orders are made and sent automatically, almost in real-time. The price evolution is analyzed by servers using advanced mathematical algorithms. This chapter will present one of these models named Price Probability Predictor. It is a method to build a probability field based on the price history and the real-time price action. The revealed function will generate the current probability of a price growth in the next time intervals. Automated entry and exit signals and market limit conditions will be built using the new indicator, in order to automate the whole investment process. Capital investment results will also be included in the current paper to qualify the presented trading methodology and to compare it with other similar models. In conclusion, it was found that the Price Probability Predictor is a reliable mathematical algorithm that can assist any trading decisions, in both ways, manual or automatic capital investments.
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