Capital investment is a sustained activity nowadays. The buy and sell decisions are usually made in technical analysis using the price quote evolution in time. Another useful information provided by any stock exchange is the trading volume for each time interval. The volume information is usually hard to be included in a trading or investment strategy, having an unstable and discontinued evolution in time. Some obsolete ideas indicate a favorable entry period after a maximal traded volume value interval, but today, on the high price volatility markets, when a maximal value is detected, usually is too late for a convenient price entry on that market. This paper presents a mathematical model specially designed for fast and instant market entry decisions based only on the traded volume information. It was found that even the traded volume variation in time is discontinued, a cyclical phenomenon is present in all markets. With the proper mathematical method, the Volume Cyclicality function can be computed in real-time in order to build reliable capital investment signals. The model presented in this paper fills an essential gap in the literature, and it was tested for more than ten years on the most important stock exchanges in the world. Investment results are also included in this paper to prove the efficiency and utility of the presented method. The Volume Cyclicality function is an exclusively mathematical model, and it can be applied in any automated investment software system to improve capital efficiency.
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.
It is well-known that the receipt of information by any person undergoes transformations, modifications and distortions depending on the personality, the experience, the level of education and culture and the habits of each person. In the activity of promoting and selling a computer service for investors, one can notice that there are several behavioural patterns. Thus, while for some clients the presentation of a capital evolution chart is a decisive factor, other investors can make a decision only after analysing the figures in the investment plan, and some others only after hearing a verbal presentation of the respective service. After a significant number of observations in this area, we were able to develop a collection of investors' behavioural patterns. The present paper presents all these psychological types of behaviour, in the investment context, accompanied by observations on the particularities of each pattern, the identification method, the appropriate manner of transmitting information and, perhaps most importantly, the elements that can positively influence the investor's decision, depending on the typology they belong to. As we are talking about an investment activity, which of course involves taking a risk, the fears that the investor is facing represent a special topic. Poorly addressed in the research literature, investor's fears are the topic of the second part of this paper. We will present you the different types of fear that investors face, the ways in which these can be identified and especially the actions by which these fears can be eliminated or controlled, so that they do not produce negative emotions during the investment. The present paper covers two important topics that are hardly addressed in the research literature. The article addresses equally to both those who work in the field of marketing and promotion of services for investors, as well as to the investors themselves, who can identify themselves as party of different typologies and thus explain their reactions and especially the fears they face. They can also identify the different ways by means of which the investment process can become stress-free.
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
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.
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.