• International Journal of Technology (IJTech)
  • Vol 11, No 6 (2020)

Generating a Multi-Timeframe Trading Strategy based on Three Exponential Moving Averages and a Stochastic Oscillator

Generating a Multi-Timeframe Trading Strategy based on Three Exponential Moving Averages and a Stochastic Oscillator

Title: Generating a Multi-Timeframe Trading Strategy based on Three Exponential Moving Averages and a Stochastic Oscillator
Lyukevich Igor Nickolaevich, Gorbatenko Irina Igorevna, Rodionov Dmitry Grigorievich

Corresponding email:


Cite this article as:
Nickolaevich, L.I., Igorevna, G.I., Grigorievich, R.D., 2020. Generating a Multi-Timeframe Trading Strategy based on Three Exponential Moving Averages and a Stochastic Oscillator. International Journal of Technology. Volume 11(6), pp. 1233-1243

898
Downloads
Lyukevich Igor Nickolaevich Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, St. Petersburg, 195
Gorbatenko Irina Igorevna Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, St. Petersburg, 195
Rodionov Dmitry Grigorievich Graduate School of Industrial Economics, Institute of Industrial Management, Economics and Trade, Peter the Great St. Petersburg Polytechnic University, Politechnicheskaya St., 29, St. Petersburg, 195
Email to Corresponding Author

Abstract
Generating a Multi-Timeframe Trading Strategy based on Three Exponential Moving Averages and a Stochastic Oscillator

This study combines a fundamental analysis of the rationale for conservative investors’ transactions, as well as long-term, low-risk strategies, and a technical analysis of the search for entry points into short-term, high-risk speculation. A hypothesis about the possible adaptation of high-risk foreign-exchange-market strategies to a low-risk stock market, based on a multi-timeframe analysis of the intersection of 3 EMA plus stochastic (a combination of three moving averages and a stochastic oscillator), is proven. The study’s modeling is based on walk-forward, blind simulation, cross procedure for realistically testing a hypothesis that can be performed in nine steps (Colby, 2003.) Colby’s algorithm Its subject is ordinary shares of Sberbank of Russia, and its analysis shows an absence of uncharacteristic movements in the chosen period of maximum volatility, from 2007 to the present. This analysis was conducted for two timeframes (more than five years for the trend direction and less than three years for the entry point). For the EMA, parameters were set at 20, 50, and 200; for stochastic parameters were set at 14, 3, and 3, 80/20. The “failure swing” reversal pattern and new support and resistance lines were detected. The study’s main conclusions are that the simultaneous use of three EMAs makes determining a corridor or a trend fairly reliable, as well as setting stop-losses. Moreover, the use of an oscillator is found not to always be reasonable; its main task is to confirm a signal. A stochastic oscillator with an explicit trend should not be analyzed for the whole period under consideration—only the last values should be considered. Moving averages and oscillators give fewer false signals on medium-term timeframes than on short-term timeframes. Due to a change in trend direction, identifying new (defined and correct) support and resistance lines is found to be necessary.

Algorithmic trading; Financial market; Moving averages; Stochastic; Technical indicators; Trading strategies

Introduction

Digital technologies’ role in the modern circular economy is becoming more and more important (Berawi, 2020). Studies have shown a link between basic human values and risk perceptions, between risk perceptions and risk behavior, and between risk behavior and accidents (Sutalaksana, 2019). Therefore, an important task is to minimize the emotional component of production processes and, in particular, to algorithmize the search for the right trading strategies.

The development of forecast price models is one of the most active application Areas (Plotnikova, 2018) .Basic models have similar objectives, based on minimizing overall costs and increasing the efficiency of available resources (Balashova and Gromova, 2017).

The aim of the current work is to create a new trading strategy for the stock market, based on well-known technical indicators. The foreign exchange market is volatile; only professional traders can accept high risks, and their strategies aim to clearly define entry and exit points. To generate this study’s strategy, the authors took a simple forex strategy as a basis and adapted it to the stock market for conservative investors. Our working hypothesis is that the trading strategy for a low-risk stock market can be improved by adapting high-risk forex strategies.

Multi-frame analysis is an assessment of a given situation from different timeframes—a skill that many market participants lack. (Singh, 2000) And this lack is significantly problematic because many investors do not consider emerging graphical patterns in global trends or the impact these patterns can have on market participants’ trading decisions in other timeframes. Thus, our explanatory hypothesis is that adapting strategies is possible on the basis of multi-timeframe analysis, in which a longer timeframe determines a trend’s direction and a shorter timeframe determines an optimal entry point.

Conclusion

The stochastic oscillator is a leading indicator, and EMAs are lagging indicators. The search for the optimal parameters for these indicators will allow a balance of the market signals, which will increase the trading strategy’s effectiveness. We have found the parameters that allow for an adaptation of high-risk forex market trading strategies to the stock market. First, these parameters include two timeframes: the higher timeframe is five years—for the trend direction—while the lower timeframe is three years—for the entry point. Second, a combination of three EMAs (20, 50, and 200 daily) plus stochastic provides a main (fast) line averaging over 14 and smoothing over three periods, as well as a signal (a slow-moving average after a fast-moving average) line with a period averaging 3, with 80% overbought and 20% oversold shares.  Third, combining EMAs as a trend indicator with a stochastic oscillator as a market speed indicator allows for the identification of patterns and the determination of the optimal points to open and close positions.

The specific quantitative results are two probable forecasts that indicate specific price levels at which to enter the market: (1) 80–90 rubles per share, and (2) 230–240 rubles per share. At the moment (November 2020), the share price is 238 rubles per share, which confirms our strategy’s reliability and potential for practical application.

Discussion. A strategy’s effectiveness can be assessed by its ratio of true and false signals, compared with other strategies for similar timeframes. The fewer false signals, the more effective the strategy. The continuation of the study in testing the strategy with other indicator parameters to find the best combination.

 The resulting forecast model is designed for a wide range of people; it is comprehensible and easy to use. This approach is how a trading algorithm can be generated and programmed for automated trading systems (per MetaTrader, InteractiveBrokers, and others) and developing a robo-advisor. 

Acknowledgement

This research was supported by the Academic Excellence Project 5-100, proposed by Peter the Great St. Petersburg Polytechnic University.

References

Achelis, S.B., 1995. Technical Analysis from A to Z: Every Trading Tool…from the Absolute Breadth Index to the Zig Zag. New York: McGraw-Hill Book Company

Arutunyan, M., Skhvediani, A., Kudryavtseva, T., Novikov, S., 2018. ARIMA Model for Describing Dynamics of Bitcoin Cryptocurrency. In: Proceedings of the 32nd International Business Information Management Association Conference 2018

Babkin, A.V., Burkaltseva, D.D., Betskov, A.V., Kilyaskhanov, H.S., Tyulin, A.S., Kurianova, I.V., 2018. Automation Digitalization Blockchain: Trends and Implementation Problems. International Journal of Engineering and Technology, Volume 7(3), pp. 254–260

Balashova, E.S., Gromova, E.A., 2017. Russian Experience of Integrating Modern Management Models. Espacios, Volume 38(53), pp. 31–39

Bataev, A.V., 2018. Innovative Forms of Interaction between Financial Institutions and Clients: Automated Banking Offices. In: Proceedings of the 3rd International Conference Ergo-2018: Human Factors in Complex Technical Systems and Environments, Ergo, pp. 9–13

Berawi, M.A., 2020. Managing Nature 5.0: The Role of Digital Technologies in the Circular Economy. International Journal of Technology, Volume 11(4), pp. 652–655

Bouayoune, K.S., Boudi, E.M., Bachir, A., 2017. A Stochastic Method based on the Markov Model of Unit Jump for Analyzing Crack Jump in a Material. International Journal of Technology, Volume 8(4), pp. 622–633

Clement, L., 1908. The Ancient Science of Numbers. New York: Roger Brothers

Colby, R.W., 2003. The Encyclopedia of Technical Market Indicators. 2nd ed. McGraw-Hill Publishing

Elliott, R.N., 1938. The Wave Principle. Alanpuri Trading, Los Angeles, CA, 2013 (originally published by R.N. Elliot, New York, NY)

Faijareon, C., Sornil, O., 2019. Evolving and Combining Technical Indicators to Generate Trading Strategies. Journal of Physics, Conference Series 1195, pp. 16–32

Forex Strategies, 2014. Stochastic Scalping with Three Moving Averages. Available Online at https://www.forexstrategiesresources.com/scalping-forex-strategies-ii/281-stochastic-scalping-with-three-moving-averages

Gann, W.D., 1941. How to Make Profits Trading in Commodities: AA Study of the Commodity Market. Lambert-Gann, Pomeroy, WA

Gusev, V.P., 2015. Japanese Candles. Application Features. [s. l.] Moscow

Hamilton, W.P., 1922. The Stock Market Barometer: A Study of Its Forecast Value Based on Charles H. Dow’s Theory of the Price Movement: With an Analysis of the Market and Its History Since 1897. New York: Harper & Bros

Huang, J.-Z., Huang, Z., 2020. Testing Moving Average Trading Strategies on ETFs. Journal of Empirical Finance, Volume 57, pp. 16–32

Kalmykova, S.V., Pustylnik, P.N., Razinkina, E.M., 2017. Role Scientometric Researches’ Results in Management of Forming the Educational Trajectories in the Electronic Educational Environment. Advances in Intelligent Systems and Computing, Issue 545, pp. 427–432

Kuporov, J.J., Kudryavtseva, T.J., Gorovoy, A.A., 2018. Algorithm for Formation of the Investment Project Portfolio of a Public Utility Company. Proceedings of the 31st International Business Information Management Association Conference, 2018

Lane, G.C., 1985. Lane’s Stochastics: The Ultimate Oscillator. CMT Association Journal (Journal of Technical Analysis), Issue 21, pp. 37–42

Lebeau, C, Lucas, D.W., 1992. Technical Traders Guide to Computer Analysis of the Futures Markets. McGraw-Hill Education

Lebedev, O.T., Mokeeva, T.V., Rodionov, D.G., 2018. Matrix Structures of Science and Technology Innovations Development and Implementation Trajectory. In: Proceedings of the 31st International Business Information Management Association Conference

Lyukevich, I., Agranov, A., Kulagina, N., 2018. Issues of Exponential Smoothing in Economical Forecasting. In: Proceedings of the 32nd International Business Information Management Association Conference, 2018

McWhirter, L., 1938. Astrology and Stock Market Forecasting. New York: ASI Publishers Inc (Second ed., 1977)

Mikula, P., 1995. Gann’s Scientific Methods Unveiled. Volume 1 and Volume 2. P. Mikula Pub. and Trading,  Austin, USA.

Mitaeva, O., (n/d) Technical Analysis: The Mysterious Methods of William Delbert Gunn. Available online at Educational Site for Investors and Traders. https://i-trading.ru/poleznoe/izvestnye-trejdery/vilyam-delbert-gann

Murphy, J.J., 1986. Technical Analysis of the Futures Markets: A Comprehensive Guide to Trading Methods and Applications. New York Institute of Finance

Naik, N., Mohan, B.R., 2019. Optimal Feature Selection of Technical Indicator and Stock Prediction using Machine Learning Technique. In: Emerging Technologies in Computer Engineering: Microservices in Big Data Analytics. ICETCE 2019., Volume 985, pp. 261–268, Singapore: Springer

Nison, S., 1994. Beyond Candlesticks: New Japanese Charting Techniques Revealed. John Wiley & Sons

Plotnikova, E.V., 2018. Investigating the Influence of Gender and Age on the Choice of Housing. In: Proceedings of the 31st International Business Information Management Association Conference, 2018

Prechter, R.R. Jr., Frost, A.J., 1991. Elliott Wave Principle: Key to Stock Market. New York: McGraw Hill Publishing Co.

Rhea, R., 1932. The Dow Theory: An Explanation of Its Development and an Attempt to Define Its Usefulness as an Aid in Speculation. New York: Barron’s

Rudskaya, I.A., Rodionov, D.G., 2018. Comprehensive Evaluation of Russian Regional Innovation System Performance using a Two-Stage Econometric Model. Espacios, Volume 39(4), pp. 40–52

Russell, R., 1961. The Dow Theory Today. New York: Richard Russell Associates, New York

Schade, G., 2005. The Origins of the Stochastic Oscillator. The Chartered Market Technician (CMT) Association. Available Online at https://cmtassociation.org/kb/origins-of-the-stochastic-oscillator-article

Singh, S.P., 2000. Modelling in Time-Series Forecasting. Cybernetics and Systems—An International Journal, Volume 31(1), pp. 49–65

Skhvediani, A.E., Kudryavtseva, T.Y., 2018. The Socioeconomic Development of Russia: Some Historical Aspects. European Research Studies Journal, Volume 21(4), pp. 195–207

Snezhko, Y.S., 2015. The Use of Technical Analysis Indicators in the Russian Stock Market. Russian Journal of Entrepreneurship, Volume 16(16), pp. 2681–2696

Sutalaksana, I.Z., Zakiyah, S.Z.Z., Widyanti, A., 2019. Linking Basic Human Values, Risk Perception, Risk Behavior and Accident Rates: The Road to Occupational Safety. International Journal of Technology, Volume 10(5), pp. 918–929

TradingView. (n/d) Free Stock Charts, Stock Quotes and Trade Ideas. Available Online at https://www.tradingview.com

Villiers, V.d., 1933. The Point and Figure Method of Anticipating Stock Prices: Complete Theory & Practice, Windsor Books, Brightwaters, New York: reprinted in 1975

Williams, L.R., 1979. How I Made One Million Dollars Last Year Trading Commodities. Windsor Books