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CAPITAL AND RISK MANAGEMENT FOR AUTOMATED TRADING SYSTEMS

Authors:
  • Algorithm Invest

Abstract and Figures

The most important part in the design and implementation process of automated trading systems in any financial investment company is the capital and risk management solution. Starting from the principle that the trading system must run fully automated, the design process gets several particular aspects. The global stop loss is a special approach for the risk management strategy that will ensures a positive expectancy in algorithmic trading. A case study based on an already optimized trading algorithm will be used to reveal how important the risk level optimization is, in order to improve the efficiency of the trading software. The main optimal criteria are as usual the profit maximization together with the minimization of the allocated risk, but these two requirements are not enough in this case. This paper will reveal an additional optimization criterion and the main directions to build a reliable solution for an automated capital and risk management procedure. Keywords: automated trading software (ATS), business intelligence systems (BIS), capital and risk management (CRM), algorithmic trading (AT), high frequency trading (HFT). (Available at: https://pauna.biz/Capital_and_Risk_Management)
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... For a specified profit target level, all functional parameters of the trading strategy will be optimized in order to maximize the profit level and to minimize the capital exposure. It was found that the third optimization criterion is "the Longest Time Trade period" [4] (LTT). "This factor makes the difference between trading and investment" [4] and is the main indicator to establish how long the capital is blocked into the recovery process of a trading strategy. ...
... It was found that the third optimization criterion is "the Longest Time Trade period" [4] (LTT). "This factor makes the difference between trading and investment" [4] and is the main indicator to establish how long the capital is blocked into the recovery process of a trading strategy. Other trading strategies use different exit methods become classical because they are used since years. ...
... To avoid these complications trading tests like those presented in the chapter 5 can be made using different valued for the ξ level. Together with the "Global Stop Loss" methodology presented in [4], the trading fragmentation method is the best method I have found in my research to reduce the capital exposure and to increase the trading efficiency using algorithmic trading in any advanced automated trading system that trade in several unrelated markets. ...
Article
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This paper presents a practical methodology to reduce the capital exposure by early exits from the financial markets using algorithmic trading. The method called trading fragmentation uses several automated trading software applied on more unrelated markets and a particular risk management strategy to obtain a higher profit level with a lower risk. An advanced capital management procedure is used to integrate all into an unitary risk management system applied into a single trading account. It was found that the method presented here is the proper way to avoid large loss trades and to reduce the time when the capital is blocked into negative positions for the recovery process. In this way the efficiency of the capital usage is improved and the profit is made faster with lower risk level. The method was tested with real capital for more than five years and positive results were obtained. Comparative trading numbers will be also included in this paper in order to reveal the efficiency and the advantages obtained with the trading fragmentation methodology.
... The DAX30 index was traded as a contract for differences (CFD) with a spread of 1 point. The exposed capital involved and the risk management were managed using the "Global Slot Loss Method" [26]. The Trigonometric Price Line was assembled for four-hour timeframe interval using 20 convergence points. ...
... The lowest capital exposure is obtained with the signals made by (12) when the price in under the trend line and a significant uptrend is present. All signals assembled together can generate a considerable number of trades and can constitute a reliable trading solution with a good RRR.In order to compare the presented capital investment methodology with other known trading strategies, comparative trading results are included in table 2. These are obtained using DaxTrader[26] between 01.07.2016 and 30.06.2019 for Frankfurt Stock Exchange DAX30 Index ...
Conference Paper
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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.
... This behavior gives us an additional optimization criterion for the δ parameter: the longest time trade period (LTT). More considerations about the LTT optimization criterion are also presented in details in (Păuna, 2018b). The functional value for the δ will be found using an optimization method to maximize the profit, to minimize capital exposure and to minimize the LTT interval. ...
... euro test capital with a maximal exposure capital of 1%. The risk and capital management were also made using the "Global Stop Loss" method (Păuna, 2018b). As can be seen in table 2, the results obtained with the HAR oversold trading signals (5) are positive. ...
Article
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Investing in capital markets is a common task today. An impressive number of traders and investors, companies, private or public funds are buying and selling every day on the free markets. The current high price volatility in the financial markets gives everyone a tremendous number of speculative opportunities to make a profit. Sometimes the price makes no significant movement, however. The majority of the trades initiated in those periods will conclude to losses or will need a very long time to become profitable. To avoid these cases, a mathematical algorithm was developed in this paper: The Silent Market Indicator. This article will present the general principles and the mathematics behind the indicator and how it can be applied in financial trading to improve capital investment efficiency. It was found that the model generates a very reliable filter to avoid entry into the silent markets intervals, when the price action conducts to small amplitude price movements and when the profit expectation is lower. In order to reveal the efficiency of the Silent Market Indicator usage, some comparable trading results will be presented in the last part of this article together with the functional parameters optimized for several known capital markets. As a conclusion, it will be proved that the presented methodology is an excellent method to stay away from the market risk. In addition, being exclusively a mathematical model, it can be applied in any algorithmic trading system, combined with any other trading strategy in order to improve capital efficiency
... The results were obtained with DaxTrader (Păuna, 2010), an automated capital investment software using the Price Probability Predictor to build automated capital investment signals. The risk and capital management were assured using the "Global Stop Loss" method (Păuna, 2018d) with a maximal risk level of 1%. ...
Conference Paper
Full-text available
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.
... For the capital investment and financial trading activities, the Logarithmic Risk Distribution can be successfully combined with the "Global Stop Loss" methodology (Pauna, 2018). Especially for the automatic trading and investment systems, the risk calibration needs a distribution function in order 771 to set the risk automatically depending on the results obtained. ...
Conference Paper
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Capital investment, trading, or any business, all are activities involving risk. A proper capital management strategy to ensure long-term profitability is valuable nowadays. High price volatility in the stock markets, unpredicted or unusual economic and geopolitical news, or just hard to manage rare resources or human factors, all of these are instability reasons which can decrease capital efficiency or even cancel the profit over time. To manage the involved risk in any economic activity is a key factor for any manager today. Whatever the risk is estimated, the basic idea is always the same. To ensure long-term profitability, the investor has to save a part of the profit and to reinvest the rest to obtain a stable capital grow in time. The question this paper will answer is how much profit to save and how much to reinvest to produce stable capital growth and sustainable capital efficiency? The Logarithmic Risk Distribution will be presented, a practical method to size the risk level depending on the invested capital, on the used capital exposure level, and on the profit already made in the current business. It was found that the risk level can depend only on these three factors through a function that will provide an exponential capital grow even if the risk is higher than the realized profit. This paper will also include examples to prove the efficiency and simplicity of the presented method. The Logarithmic Risk Distribution is simple and easy to be applied in any business or investment.
... For simplicity, the trades were made into an account with no leverage. The capital risk was managed using the "Global Slot Loss Method" [26]. The trading signals in Table I were assembled into four-hour time frame interval. ...
Conference Paper
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 simplicity, the trades were made into an account with no leverage. The capital risk was managed using the "Global Slot Loss Method" [26]. The trading signals in Table I were assembled into four-hour time frame interval. ...
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.
... The data stream with the available liquidity is provided by each brokerage informational system and will be subject of the same ETL methodology presented in paragraph 5.3. A reliable model for the risk and capital management specially designed for real-time processing and automated trading services are presented in [46]. The real-time service processing will aggregate the data and transformed into trading orders: Depending on the available capital from each client accounts and the risk level granted by contract by each client, the capital exposure methodology will decide about the trading volume. ...
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Business intelligence systems represent a significant trend today. Choosing the right project management methodology is an essential step for a successful business intelligence implementation. New aspects and perspectives are included in this process nowadays due to new requirements imposed by the real-time activities. The automated decision-making systems used in different activity domains and the low-latency responses required by different processes determine new specifications for the entire system. The response delay of each time chain component has become a design factor. Also, using automated decision-making systems, the human factor is excluded from an important part of the decision process. To manage the decision tree appropriately, the human and automated decisions units must also be included in the business intelligence system design. It was found that the results obtained after the implementation of a real-time decision system will conduct to new requirements for the business intelligence system itself and will produce new resources for a better and improved solution. This progressive implementation needs a suitable management methodology in order to permit evaluative adaptability for the entire system. This paper will present the Progressive Management Methodology especially designed for a successful Real-Time Business Intelligence Decision System implementation. The model permits the analysis, design, implementation, and improvement for the real-time components considering the time-delay as a design factor.
Article
El objetivo de este trabajo es desarrollar un Autómata Evolutivo (AE) que opera con base a un modelo de martingalas con el que se definen estrategias de inversión, las cuales utilizan información inmediata histórica, límites de ganancia, pérdida y tiempos de permanencia; brinda señales de compra, venta o mantener la posición del activo basadas en la combinación óptima de medias móviles seleccionadas mediante un algoritmo genético. Se probó para dos índices accionarios antes, durante y después de la crisis subprime, mostrando que, cuando los mercados estaban en fase alcista, la estrategia comprar-manter (BH) generó un rendimiento superior al AE; en contraste, para el periodo de crisis observado, el AE logró un rendimiento mayor; finalmente, en todo el periodo de prueba, el rendimiento del AE fue superior. El AE tiene la restricción que solo puede ser usado por una acción/índice por periodo, aunque esto puede ser solventado al ciclarlo por el número de instrumentos del portafolio. La autenticidad del trabajo radica en la combinación de modelos que se complementan para generar un sistema que ayuda en la toma de decisiones.
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.
Book
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The implementation of sound quantitative risk models is a vital concern for all financial institutions, and this trend has accelerated in recent years with regulatory processes such as Basel II. This book provides a comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management and equips readers--whether financial risk analysts, actuaries, regulators, or students of quantitative finance--with practical tools to solve real-world problems. The authors cover methods for market, credit, and operational risk modelling; place standard industry approaches on a more formal footing; and describe recent developments that go beyond, and address main deficiencies of, current practice.The book's methodology draws on diverse quantitative disciplines, from mathematical finance through statistics and econometrics to actuarial mathematics. Main concepts discussed include loss distributions, risk measures, and risk aggregation and allocation principles. A main theme is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. The techniques required derive from multivariate statistical analysis, financial time series modelling, copulas, and extreme value theory. A more technical chapter addresses credit derivatives. Based on courses taught to masters students and professionals, this book is a unique and fundamental reference that is set to become a standard in the field.
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