CC BY
20РАСЧЕТ И АНАЛИЗ МЕХАНИЧЕСКОЙ ТОРГОВОЙ СИСТЕМЫ НА ОСНОВЕ ТЕХНИЧЕСКИХ ИНДИКАТОРОВ
Каримов Э.О.
Магистрант,
Финансовый университет при Правительстве Российской Федерации
CALCULATION AND ANALYSIS OF MECHANICAL TRADING SYSTEM BASED ON TECHINCAL
INDICATORS
Karimov E.
Master student,
Financial University under the Government of the Russian Federation
Аннотация
В связи с переходом на рыночные отношения и усилением интеграционных процессов России в мировое экономическое пространство, особую роль приобретают фондовые рынки как механизм аккумулирования и перераспределения ликвидности и активов, что способствует притоку трейдеров на рынок, целью который является получение приращенной прибыли на вложенный капитал. На сегодняшний день существует достаточно обширная база предлагаемых торговых стратегий, построение которых основывается на классических технических индикаторах. Тем не менее, многие трейдеры продолжают терять свой капитал. Таким образом, усовершенствование методик и стратегий торговли требует расширение научного познания. В данной статье мы оценим эффективность торговой системы, построенной на основе популярных методов технического анализа, таких как уровни поддержки и сопротивления, стохастический осциллятор, экспоненциальное скользящее среднее, с помощью методов исторического моделирования, бэкте-стинга. В результате, торговая система продемонстрировала хорошие финансовые результаты, в том числе благодаря внедрению системы риск-менеджмента.
Abstract
In connection with the transition to market relations and strengthening of integration processes of Russia into the world economic space, stock markets as a mechanism for accumulation and redistribution of liquidity and assets, which contributes to the inflow of traders to the market, the purpose of which is to obtain increased profits on invested capital. Nowadays, there is a wide enough base of proposed trading strategies, the construction of which is based on classical technical indicators. Nevertheless, many traders continue to lose their capital. Thus, the improvement of trading methods and strategies requires an increase in scientific knowledge. In this article we will evaluate the effectiveness of a trading system based on popular technical analysis methods, such as support and resistance levels, stochastic oscillator, exponential moving average, using historical modeling methods, back testing. As a result, the trading system has shown good financial results, including the implementation of risk management system.
Ключевые слова: механическая торговая система, стохастик, экспоненциальное скользящее среднее, историческое моделирование, бэктестинг, алгоритмическая торговля, фондовый рынок.
Keywords: mechanical trading system, stochastics, exponential moving average, historical modeling, back testing, algorithmic trading, stock market.
I. Introduction
Russian stock market is one of the most undervalues markets, that implies that it has significant opportunity for growth. In general, due to not sufficient amount of trading agents and the early stage of development of stock market in Russian Federation it could be characterized as a semi-efficient market. Taking that into account, it is possible to state that relatively low market efficiency could enable traders to capitalize on that using not so complicated technical indicators and trading rules. During the following research we would test simple mechanical trading system based on classical technical analysis indicators against historical data
II. Construction of trading system
First of all, lets define what rules should any trading system contain. In the general case these rules are:
1. Position entry rules - define entry points.
2. Rules of exit from position - define exit points
3. Protective stop of trading - exit with loss
4. Capital management rules - position size determination
It is necessary to define support and resistance lines. It should be noted that we will take not only the specified price level, but "zones" for the levels. The resistance and support zone are calculated as a given price level +/- 5% - 10%.
Each of these rules must contain a certain logic, which ensures that each rule works. If we want to participate in market movements, we must enter the market on the basis of some indication that makes up the entry rules. Accordingly, if we have entered the market, we need to exit it at some point. When it happens, it depends on the system features. The exit itself can be of two types - stop trading (so-called stop-loss), or exit with profit.
In general, the rules of exit and protective stop of trade are separated, but they are often applied together. For example, work with moving averages: enter a long position if the moving average with a smaller period
crosses the moving average with a larger period from bottom to top, and exit - if the moving average with a smaller period crosses the moving average with a larger period from top to bottom. For short positions it is exactly the opposite.
1. First of all, it is necessary to define support and resistance lines. It should be noted that we will take not only the specified price level, but "zones" for the levels. The resistance and support zone are calculated as a given price level +/- 5% -10%.
2. Exponential Moving Averages.
In our case EMA parameters: 9 (short-term trend),36 (medium-term trend) and 72 (long-term trend).
Using the price oscillator stochastics to confirm market entry. Stochastics parameters: 14,5,3. To confirm market entry, we will take a crossover of the fast and slow signal line in (near) the oversold and overbought zones or divergence of the price and oscillator (Picture 18,29).
Picture 1. Cross of fast and slow stochastics line.
Picture 2. Divergence of price and oscillator
3. Stop-loss positioning
This system implies the setting of moving stop-losses (i.e. it is necessary to move the price value of the stop-loss position in accordance with the share price). In our case, the stop-losses are placed outside the longest EMA with parameters 72.
Market entry conditions.
The beginning of a new cycle should be considered as a breakout/breakout of resistance/support lines. To enter and exit the market, we will use the intersection of all EMAs and, depending on the future trend, to build the EMA in order: in an uptrend - a short-term EMA at the top, at the bottom - a long-term EMA and vice versa. For confirmation we will use the following rule: if the price has crossed the EMA, but the EMA has not crossed each other, then this configuration corresponds to the correction waves.
For confirmation of EMA signals, we use stochas-tics: when building EMA, it is necessary to wait for either the turn of the fast and slow signal zone and their
exit from the oversold/bought zones, or to identify the divergence if it is present.
Criteria for evaluating the trading system.
One of the most important criteria of any trading system is profitability for the selected period. Profitability is calculated according to the following formula: TotalReturn=
StartEquity
Where,
TotalReturn - profitability for the period
StartEquity- size of initial capital
EndingEquity amount of capital at the end of the period
Besides, the size of the maximum drawdown is an important parameter of the trading system evaluation. This parameter allows to predict what maximum loss a trader can suffer.
The third parameter determines the number of deals for the selected period: the number of winning positions and losing positions. Winning positions should dominate.
8 Screenshot from trading terminal QUICK
9 Screenshot from trading terminal QUICK
III. Calculation of trading system
For the analysis of a trading short-term strategy, the instruments for analysis will be Russian shares traded on the Moscow Exchange. We will test our trading system on the shares which were diversified by sectors of the economy: Aeroflot, Sberbank, Mostotrest,
PhosAgro, Levenguk. When trading, we will use capital of 200,000 rubles per share.
We will summarize our transactions in a table. (Table 1)
Table 1.
Trade deals
Stock Date Price (Buy) Price () Number of shares Profit Profit, rubles
Aeroflot 15.10.1524.03.17 Buy 40,52 Sell 154,7 4500 +114,5 515250
28.04.1727.06.17 Buy 167,29 Sell 172,9 1195 +5,61 6703,95
31.10.172.03.18 Sell 179,88 Buy 151,21 1111 +28,67 31852,37
9.07.184.12.18 Sell 140,15 Buy 107,07 1427 +33,08 47205
Sberbank 1.10.1512.01.16 Buy 73,93 Sell 92,38 2700 +18,45 49815
17.02.1722.02.17 Buy 98,32 Sell 162,81 2034 +63,78 129728
17.080173.04.18 Buy 168,95 Sell 245,44 1183 +79,49 90842,57
16.05.1810.07.18 Sell 232,79 Buy 228,6 859 +4,19 3599,21
6.03.1924.07.19 Buy 205,11 Sell 229,43 975 +24 23400
9.12.197.03.19 Buy 234,85 Sell 246,60 851 +11,75 9999
Mostotrest 2.04.1512.08.15 Buy 76,63 Sell 86,10 2609,9 +9,47 24715,75
11.09.154.12.15 Sell 85,24 Buy 79,98 2346 +5,26 12339,9
24.03.1618.05.16 Buy 80,26 Sell 88,40 2491 +8,14 20276,74
26.12.1620.06.17 Buy 88,74 Sell 104,73 2253 +15,99 36025,47
2.08.1712.01.18 Buy 108,17 Sell 147,34 1430 +44,87 6416,1
20.04.1814.12.18 Sell 139,82 Buy 94,95 1430 +44,87 64164,1
PhosAgro 30.07.1515.10.15 Buy 2150 Sell 2569 93 +419 38967
6.07.1610.11.16 Sell 2749 Buy 2532 72 +217 15624
It should be noted that Levenguk has not passed the test, as the prices of this company have high daily price volatility, which associated with constant beating out the stop-loss.
Thus, as a result of speculative trading the profit at a rate of 1 126 924,06 rubles has been received, accordingly profitableness will make 112,7 % (average annual profitability 22,54 %).
IV. Conclusion
In summary, let's draw the following conclusions. We have analyzed strategy that is based on application of classical technical analysis using technical indicators and oscillator. As a result of the analysis it was found out that the use of classical trading gives a yield of 112,7% for all period and 22,54% per year. However,
in practice many traders do not achieve comparable returns, which could be the result of disregarding risk-management techniques and behavioral deviations that are inherent to traders as a result of trading. Further investigation of the reasons that lay beyond decreased individual traders' financial results deserves separate scientific research.
References
1. Park C.H., Irwin S.H. What do we know about the profitability of technical analysis? // Journal of Economic Surveys. 2007. Vol. 21. No. 4. P. 786-826.
2. Tsai C.F. et al. Predicting stock returns by classifier ensembles // Applied Soft Computing. 2011. Vol. 11. No. 2. P. 2452-2459.
3. Tsai C.F., Hsiao Y.C. Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches // Decision Support Systems. 2010. Vol. 50. No. 1. P. 258269.
4. Zhai Y., Hsu A., Halgamuge S. K. Combining news and technical indicators in daily stock price trends prediction // International symposium on neural networks. Springer, Berlin, Heidelberg, 2007. P. 10871096.
5. Zhu Y., Zhou G. Technical analysis: An asset allocation perspective on the use of moving averages // Journal of Financial Economics. 2009. Vol. 92. No. 3. P. 519-544.
6. Кашина О.И. Сравнительный анализ инвестиционных стратегий участников фондового рынка // Актуальные вопросы экономических наук: материалы междунар. науч. конф. (г. Уфа, октябрь 2011 г.). Уфа: Лето, 2011. - C. 53-56.
7. Белых Е.А. Практические аспекты применения технического анализа при построении торговых систем // Интеллектуальный потенциал XXI века: ступени познания. 2013. №. 17. С. 122-132.
8. Бирюкова Е.И. Инновационные финансы российского рынка и их использование в инвестиционных стратегиях // Имущественные отношения в Российской Федерации. 2012. №. 9. С. 6-14.
9. Володин С.Н., Баулин А.Г. Эффективность технического анализа на различных временных горизонтах инвестирования // Фондовый рынок: современное состояние, инструменты и тенденции развития. М.: Бизнес Элайнмент. 2012. С. 45-55.
10. Официальный сайт World Federation of Ex-changes.URL: https://www.world-exchanges.org
11. Официальный сайт финансового брокера «Финам»: URL: www.finam.ru
ESSENCE, CRITERIA AND INDICATORS OF DEVELOPMENT OF LARGE-SCALE
AGRICULTURAL PRODUCTION.
Kolesnikov A.
doctor of Economics, Professor of the Russian Academy of Sciences, Нailar, China, Hulunbuir Institute
Abstract
There are two forms of development: evolution-associated with gradual quantitative changes in the object: revolutionary-characterized by qualitative changes in the structure of the object. The main components of the term "development" are: the presence of a goal (direction of development), the time or time frame allotted for changing the object, taking into account objective historical, economic and other laws. The first component of development is the presence of goals and objectives. It is the correct goal, optimally solved tasks, and implementation of a set of measures that allow the object to develop progressively. A well-defined goal and clear ideas about the final result sometimes provide the key to solving all the planned tasks, while speeding up not only the development process, but also its quality. Of course, the main goal of any commercial enterprise is to get the maximum profit, but in different areas of activity, tasks to achieve the goal are solved in different ways. Sometimes, the solution of such problems leads the company's management to a standstill.
Keywords: evaluation criteria for large-scale production, indicators for the assessment of large-scale production in agriculture.
For example, in 2017, the gross grain harvest was a record for the last 40 years, but the sales price did not cover even the expenses incurred by producers, not to mention maximizing profits. Therefore, for the progressive development of agricultural production, it is necessary to take into account a set of factors that affect the final result. And the more complete this accounting is, the more predictable the enterprise development process becomes. Factor accounting and calculation of indicators should be carried out using various methodological techniques, which will ultimately ensure the objectivity of such accounting, show the level of influence of factors on the final result, possible trends, dependencies, relationships, and allow you to build models. Development is possible without the presence of targets, without taking into account historical and economic laws, which is present today in many agricultural organizations. However, in this case, development will flow slowly, inefficiently, non-linearly, with many re-
gressive thresholds and branches. The existence of targets presupposes a progressive development over time and taking into account objective laws.
An important part of development is the time or time frame needed to change an object. This is primarily due to the fact that objects and ongoing processes can't be changed right now. This type of transformation requires a certain time frame [1,c.3]. Moreover, in modern conditions, it is not physical time, not chronology, but rhythm, the level of intensity of development that comes to the fore. It is the rhythm, intensity, that in practice determines the level of production efficiency, the rhythm and quality of its development. A striking example is the development of the poultry industry. Until 1998, this industry was unprofitable for various reasons. However, the industry's advantage is a short production cycle of approximately 50-60 calendar days. This advantage has contributed to the development of poultry farming in a number of Russian regions. This advantage was used by investors. It is profitable for investors to develop those industries that give
