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