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Price Probability Predictor. Capital Investments Assisted by a Probability Field
Abstract
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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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.
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.
One of the most popular trading methods used in financial markets is the Turtle strategy. Long-time passed since the middle of 1983 when Richard Dennis and Bill Eckhardt disputed about whether great traders were born or made. To decide the matter, they recruited and trained some traders (the Turtles) and give them real accounts and a complete trading strategy to see which idea is right. That was a breakout trading strategy, meaning they bought when the price exceeded the maximum 20 or 50 days value and sold when the price fell below the minimum of the same interval. Since then many changes have occurred in financial markets. Electronic trading was widespread released and financial trading has become accessible to everyone. Algorithmic trading became the significant part of the trading decision systems and high-frequency trading pushed the volatility of the financial markets to new and incredible limits nowadays. The orders are built and sent almost instantly by smart computers using advanced mathematical algorithms. With all these changes there are many questions today regarding the breakouts strategies. Are the Turtle rules still functional? How can the Turtle strategy be automated for algorithmic trading? Are the results comparable with other modern trading strategies? After a short display of the history and the system's rules, this paper will find some answers to all these questions. We will reveal a method to automate a breakout strategy. More different trading strategies originating from the Turtle rules will be presented. A mathematical model to build the trading signals will be described in order to automate the trading process. It was found that all of these rules have a positive expectancy when they are combined with modern limit conditions. The paper will also include trading results obtained with the methods presented in order to compare and to analyze this capital investment methodology adapted especially for algorithmic trading.
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
After the widespread release of electronic trading, automated trading systems have become a significant part of the business intelligence system of any modern financial investment company. An important part of the trades is made completely automatically today by computers using mathematical algorithms. The trading decisions are taken almost instantly by logical models and the orders are sent by low-latency automatic systems. This paper will present a real-time price prediction methodology designed especially for algorithmic trading. Based on the price cyclicality function, the methodology revealed will generate price cyclicality bands to predict the optimal levels for the entries and exits. In order to automate the trading decisions, the cyclicality bands will generate automated trading signals. We have found that the model can be used with good results to predict the changes in market behavior. Using these predictions, the model can automatically adapt the trading signals in real-time to maximize the trading results. The paper will reveal the methodology to optimize and implement this model in automated trading systems. After tests, it is proved that this methodology can be applied with good efficiency in different timeframes. Real trading results will be also displayed and analyzed in order to qualify the methodology and to compare it with other models. As a conclusion, it was found that the price prediction model using the price cyclicality function is a reliable trading methodology for algorithmic trading in the financial market.
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.
Buy cheap and sell more expensive. This is the main principle to make a profit on capital markets for hundreds of years. The rule is simple but to apply it in practice has become a very difficult task nowadays, with very high price volatility in the financial markets. Once electronic trading was widespread released, reliable solutions can be found using algorithmic trading systems. This paper presents a data mining method applied to the time price series in order to generate buy and sell decisions using computational algorithms. It was found that an original data mining method based on the price cyclicality function gives us an important profit edge when it is about the capital investments on the short and medium term. The Cyclical Trading Method will be presented together with the main principles and practices to design and optimize trading software. Test results are also included in this article in order to compare the presented method with other known methodologies to trade the capital markets.
Trading and investment on financial markets are common activities today. A very high number of investors, companies, public or private funds are buying and selling every day with a single purpose: the profit. The common questions for any market participant are: when to buy, when to sell and when is better to stay away from the market risk. In order to answer all these questions, many trading strategies are used to establish the best moments to entry or to exit the trades. Due to the large price volatility, a significant part of the trades is set up automatically today by computers using algorithmic trading procedures. For this particular field, special aspects must be met in order to automate the trading process. This paper presents one of these mathematical models used in automated trading systems, a method based on the Fisher transform. A general form of this method will be presented, the functional parameters and the way to optimize them in order to reduce the risk. It will be also suggested a method to build reliable trading signals with the Fisher function in order to be automated. Three different trading signal types will be explained together with the significance of the functional parameters in the price field. A code sample will be included in this paper to prove the simplicity of this method. Real results obtained with the Fisher trading signals will be also presented, compared and analyzed in order to show how this method can be implemented in algorithmic trading.
Automated trading software is a significant part of the business intelligence system in a modern investment company today. The buy and sell orders are built and sent almost instantly by computers using special trading and computational strategies. The trading decisions are made by automated algorithms. In this paper it will be presented one of these mathematical models which generate trading signals based only on the time price series. The algorithm combines several known computing techniques to build a trading indicator to automate the trades. With this method, buy decisions on oversold intervals and sell decisions on overbought price values can be built. Limit conditions in order to close the long and short trades can be also automatically generated. More trading signal types based on this model will be revealed. Trading results obtained with all these signals will be presented in order to qualify this methodology developed especially for algorithmic trading.
Trading the financial markets is a common idea nowadays. Millions of market participants, individuals, companies or public funds are buying and selling different equities in order to obtain profit from the buy and sell price difference. Once the equity was established, the main question marks are when to buy, when to sell and how long to keep the opened positions. This paper will present a mathematical model for the cyclicality of the price evolution. The model can be applied for any equity in any financial market, using any timeframe. The method will gives us information about when is good to buy and when is better to sell. The price cyclicality model is also a method to establish when the price is approaching to change its behavior in order to build limit conditions to stay away the market and to minimize the risk. The fundamental news is already included in the price behavior. Being exclusively a mathematical model based on the price evolution, this method can be easily implemented in algorithmic trading. The paper will also reveal how the cyclicality model can be applied in automated trading systems and will present comparative results obtained in real-time trading environment.
Heikin-Ashi is the Japanese term for "average bar". This methodology is well known as one of the methods to identify and follow the trends using a price time series in financial markets. Nowadays, in the first decades of the 21st century, in the electronic trading environment, with very volatile price market conditions, using the Heikin-Ashi method gets new and special connotations especially when it is about the high-frequency trading. It was found that combining the classical Heikin-Ashi candlesticks with modern limit conditions reliable trading algorithms can be generated in order to produce a good trading return with automated trading systems. This paper will present several trading algorithms based on Heikin-Ashi method for algorithmic trading especially adapted for high-frequency trading systems. It will be revealed how the trading signals can be automatically built and used in order to automate the trading decisions and orders. Exit signals will also be discussed. Trading results obtained with the presented algorithms for Frankfurt Stock Exchange Deutscher Aktienindex Market will be displayed in order to qualify the methods and to compare them with any other trading strategies for high-frequency trading. As conclusions, Heikin-Ashi combined with special limit conditions can generate reliable trading models for algorithmic trading.
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)
The mean square error of prediction is calculated for an exponentially weighted moving average (e.w.m.a.), when the series predicted is a Markov series, or a Markov series with superimposed error. The best choice of damping constant is given; the choice is not critical. There is a value of the Markov correlation ρ0 below which it is impossible to predict, with an e.w.m.a., the local variations of the series. The mean square error of an e.w.m.a. is compared with the minimum possible value, namely that for the best linear predictor (Wiener). A modified e.w.m.a. is constructed having a mean square error approaching that of the Wiener predictor. This modification will be of value if the Markov correlation parameter is negative, and possibly also when the Markov parameter is near ρ0.
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