Content uploaded by Cristian Mihail Păuna
Author content
All content in this area was uploaded by Cristian Mihail Păuna on May 23, 2018
Content may be subject to copyright.
A preview of the PDF is not available
CAPITAL AND RISK MANAGEMENT FOR AUTOMATED TRADING SYSTEMS
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)
Figures - uploaded by Cristian Mihail Păuna
Author content
All figure content in this area was uploaded by Cristian Mihail Păuna
Content may be subject to copyright.
... In our tool, we incorporate trading risk management principles that are widely adopted by stock traders and shown to help reduce potential losses and generate substantial profits [29,30]. We mainly consider three types of principles: (1) setting a limit for the maximum capital to be committed per trade (to control the maximum risk to be taken per trade), (2) setting stop-loss points to limit losses resulting from unprofitable trades, and (3) setting take-profit points to ensure profits before a stock price reverts [28][29][30]. For a given stock symbol, our trading strategy consists of the following steps: ...
... By comparing our framework with the buy-hold strategy, reported in Tables 7 and 9, we can indicate the superiority of our method over the buy-hold strategy, especially when the stop-loss and take-profit points used by our method are fine-tuned and optimized using previous data. In fact, our results suggest that there is a significant impact of the stop-loss (S/L) and take-profit (T/P) points, which is similar to what is discussed in prior work [28][29][30]. Selecting suitable values, by analyzing market volatility, exploring technical indicators, and optimizing the parameters on period). is potentially can lead to overestimating the performance of our method as no economic recession or market decline was observed during that period. erefore, our future work will attempt to address this limitation by exploring several directions; for instance, examining a set of stock market symbols that are trending down and measuring the effectiveness of our method while being used for automated trading in the stock market. ...
Stock markets are becoming the center of attention for many investors and hedge funds, providing them with a wide range of tools and investment opportunities to grow their wealth and participate in the economy. However, investing in the stock market is not trivial. Stock traders and financial advisors are required to frequently monitor market actions, search for profitable companies, and analyze stock price movements to generate various trading ideas (e.g., selecting a stock symbol and making the decision when to enter or exit a trade), potentially leading to investment returns. Therefore, this study aims to address this challenge through exploring the adaptation of machine learning methods combined with risk management techniques to develop a framework for automating the task of stock trading. We evaluated our framework by creating a diverse portfolio containing several companies listed on the Saudi Stock Exchange (Tadawul) and using the simulated trading actions (executed by the framework) to estimate the portfolio’s returns for 3.7 years. The findings show that in terms of investment returns, the proposed framework is very promising; it has generated over 86% returns and outperformed almost all hedge funds by the top investment banks in Saudi Arabia.
... 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 ...
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 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%. ...
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. ...
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. ...
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. ...
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.
Penelitian ini bertujuan untuk menguji pengaruh manajemen risiko dan strategi investasi terhadap profitabilitas dalam perdagangna forex. Penelitian ini bersifat prediktif dan menggunakan metode kuantitatif. Data dianalisis menggunakan Structural EEquation Modeling (SEM) dan optimasi Partial Least Square (PLS) dengan Smart PLS 4. Penelitian ini menggunakan manajemen risiko dan strategi investasi sebagai variabel independen, sedangkan profitabilitas perdagangan forex sebagai variabel dependen. Hasil penelitian ini menunjukkan bahwa manajemen risiko dan strategi investasi keduanya memengaruhi profitabilitas perdagangan foex, meskipun manajemen risiko memengaruhi profitabilitas perdagangan forex, namun tidak signifikan. Namun, strategi investasi, di sisi lain, memengaruhi profitabilitas perdagangan forex secara signifikan. This study aims to examine the influence of risk management and investment strategy toward profitability of forex trading. This study is a predictive study which uses quantitative method. Data was analyzed by using Structural Equation Modeling (SEM) and optimization of Partial Least Square (PLS) with Smart PLS 4. This study examines risk management and investment strategy as independent variables, and profitability of forex trading as dependent variable. This study suggests that risk management and investment strategy influence profitability of forex trading, although risk management doesn’t influence profitability of forex trading significantly. However, investment strategy, on the other side, influences profitability of forex trading significantly.
This study aims to analyze the effect of risk management and investment strategy on online forex trading, as well as the effect of risk management, investment strategy, and online forex trading on profit trading. The type of research used is an explanatory survey. The unit of analysis is members of the trader community in Banjarbaru, South Kalimantan, totaling 178 people with the sampling technique used is purposive sampling totaling 100 respondents. The variables used are risk management (X1), investment strategy (X2), online forex trading (Z), and profit trading (Y) using the path analysis method. The results of the study state that risk management has a significant effect on online forex trading, risk management has a significant effect on profit trading, investment strategies have a significant effect on online forex trading, investment strategies have a significant effect on profit trading, and online forex trading has a significant effect on profit trading.
Evolutionary Automaton (AE) for the Stock Market Using Martingales and a Genetic Algorithm
The aim of this paper is to develop an Evolutionary Automata (EA) that operates considering a martingale model, which defines investment strategies using immediate historical information, profit or lost limits and stopping time. The EA offers signals to buy, sell or hold a determinate stock, these signals are based on the optimal combination of simple moving average through a genetic algorithm. The EA was tested with two stock indices, before, during and after the subprime crisis. Finding that when the stock markets were in bull market, the Buy-Hold (BH) strategy presented superior performance than the EA; in contrast, during the crisis period, the performance of the EA was better than BH, finally, for all of the test period, the performance of the EA was superior. The EA have the restriction that can be used by one stock/index for period, though this can be solved by cycling the number of instruments of the portfolio. The authenticity of this research is the combination of the above models that match between them in order to generate a system that helps investor decisions.
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.
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.
Short term trading strategies that work
- L Connors
- C Alvarez
L. Connors, C. Alvarez, "Short term trading strategies that work", TradingMarkets
Publishing Group, 2009, ISBN 978-0-9819239-0-1, pp. 31-35.
High-frequency trading: a practical guide to algorithmic strategies and trading systems
- I Aldridge
I. Aldridge, "High-frequency trading: a practical guide to algorithmic strategies and
trading systems", Wiley, 2013, ISBN 978-1-118-34350-0, pp. 230.
Forex essentials in 15 trades
- J M Bland
- J M Meisler
- M D Archer
J. M. Bland, J. M. Meisler, M. D. Archer, "Forex essentials in 15 trades", John Wiley &
Sons, Inc., 2009, ISBN 978-0-470-29263-1, pp. 12.
The little book of Currency trading -how to make big profits in the world of Forex
- K Lien
K. Lien, "The little book of Currency trading -how to make big profits in the world of
Forex", John Wiley & Sons, Inc., 2011, ISBN 978-0-470-77035-1, pp. 104.
High probability trading setups for the currency market
- K Lien
- B Schlossberg
K. Lien, B. Schlossberg, "High probability trading setups for the currency market",
[online] 2006, Available: https://investopia.com [January 2, 2018], pp. 15-16.
Guide to conquering the markets
- M Etzkorn
M. Etzkorn, "Guide to conquering the markets", M Gordon Publishing Group, 2000,
ISBN 1-893756-06-8, pp. 146.
High performance trading
- S Ward
S. Ward, "High performance trading", Harriman House, 2009, ISBN 978-1-905641-61-1, pp. 137.