Tato práce pojednává o algoritmickém obchodování a jeho aplikaci na krypto-měnách. Algoritmické obchodování slouží ke generování kupních a prodejních signálů nebo pomocí počítačových algoritmů a řízení těchto příkazů bez zásahu člověka. Jak krypto-měny, tak algoritmické obchodování jsou součástí nedávného vývoje na finančních trzích. V této studii je zpětně testováno několik obchodních strategií založených na technických ukazatelích. Proces zpětného testování neboli backtesting spočívá v hodnocení výkonnosti algoritmů na historických datech. Tento proces je taky užitečný pro zlepšení těchto strategií. Strategie jsou poté porovnány s klasickou strategií nákupu a držení. Strategie se zdají být méně riskantní než držet jednu pozici po dlouhou dobu. Strategie poskytují konzistentní výsledky, zejména s párem LTC / USD, pro BTC/USD, i když většina strategií byla zisková, jedna z nich byla nedostatečně výkonná.
Anotace v angličtině
This work treats algoritmic trading and its application on the cryptocurrencies. Algoritmic trading is refering to the generation of buy and sell signals or orders by a computer algorithm and the management of these orders, without human intervention. Both cryptocurrencies and algoritmic trading part of the recent advancment in financial markets. A few trading strategies based on technical indicators are backtested in this study. The process of backtesting consist of evaluating the performance of algorithms on historical data. It is also usefull for improving these strategies. The strategies are then compared to a classic buy and hold strategy. The strategies appear to be less risky than holding one position over a long period of time. The strategies provide consistent results especially with the LTC/USD pair, for the BTC/USD even though most strategies were profitable, one of them was underperforming.
Klíčová slova
Algoritmické obchodování, technické indikátory, backtesting, analýza trhu, strategie, kupovací a prodávací signály
Tato práce pojednává o algoritmickém obchodování a jeho aplikaci na krypto-měnách. Algoritmické obchodování slouží ke generování kupních a prodejních signálů nebo pomocí počítačových algoritmů a řízení těchto příkazů bez zásahu člověka. Jak krypto-měny, tak algoritmické obchodování jsou součástí nedávného vývoje na finančních trzích. V této studii je zpětně testováno několik obchodních strategií založených na technických ukazatelích. Proces zpětného testování neboli backtesting spočívá v hodnocení výkonnosti algoritmů na historických datech. Tento proces je taky užitečný pro zlepšení těchto strategií. Strategie jsou poté porovnány s klasickou strategií nákupu a držení. Strategie se zdají být méně riskantní než držet jednu pozici po dlouhou dobu. Strategie poskytují konzistentní výsledky, zejména s párem LTC / USD, pro BTC/USD, i když většina strategií byla zisková, jedna z nich byla nedostatečně výkonná.
Anotace v angličtině
This work treats algoritmic trading and its application on the cryptocurrencies. Algoritmic trading is refering to the generation of buy and sell signals or orders by a computer algorithm and the management of these orders, without human intervention. Both cryptocurrencies and algoritmic trading part of the recent advancment in financial markets. A few trading strategies based on technical indicators are backtested in this study. The process of backtesting consist of evaluating the performance of algorithms on historical data. It is also usefull for improving these strategies. The strategies are then compared to a classic buy and hold strategy. The strategies appear to be less risky than holding one position over a long period of time. The strategies provide consistent results especially with the LTC/USD pair, for the BTC/USD even though most strategies were profitable, one of them was underperforming.
Klíčová slova
Algoritmické obchodování, technické indikátory, backtesting, analýza trhu, strategie, kupovací a prodávací signály
Cíl:
- I am going to compare various algorithmic trading strategies for trading BTC/USD, LTC/USD
- I am going to backtest strategies based on numerous indicators as RSI, MACD, Stocahstic oscillator
- The performance of the chosen algorithmic trading strategies (in terms of total revenues/losses) will be compared in the respective time-periods and overall and the buy and hold strategy will be used as a universal benchmark.
Metody:
- I will be using a simple platform as excel for the calculation of indicators employed for the forecasting of future price movements
- In this platform I will also create the trading rules of algorithmic trading strategies
- One of my strategies consist of comparing the highest or lowest points of a BTC trend, with an indicator like RSI, when the extremums of RSI form a line that diverge with the extremums of the BTC trend, the program (called the expert advisor) will recognize it and perform a trade
Data:
- I am going to work with data provided in the metatrader platform by the avatrade company
- The research will only be done with historical data, which are known before the application of the algorithms
- Only the 2017-2019 period will be taken into account for testing the strategies
- I am going to consider data from: www.coinmarketcap.com as well
- 4hours data will be used
Zásady pro vypracování
Cíl:
- I am going to compare various algorithmic trading strategies for trading BTC/USD, LTC/USD
- I am going to backtest strategies based on numerous indicators as RSI, MACD, Stocahstic oscillator
- The performance of the chosen algorithmic trading strategies (in terms of total revenues/losses) will be compared in the respective time-periods and overall and the buy and hold strategy will be used as a universal benchmark.
Metody:
- I will be using a simple platform as excel for the calculation of indicators employed for the forecasting of future price movements
- In this platform I will also create the trading rules of algorithmic trading strategies
- One of my strategies consist of comparing the highest or lowest points of a BTC trend, with an indicator like RSI, when the extremums of RSI form a line that diverge with the extremums of the BTC trend, the program (called the expert advisor) will recognize it and perform a trade
Data:
- I am going to work with data provided in the metatrader platform by the avatrade company
- The research will only be done with historical data, which are known before the application of the algorithms
- Only the 2017-2019 period will be taken into account for testing the strategies
- I am going to consider data from: www.coinmarketcap.com as well
- 4hours data will be used
Seznam doporučené literatury
Taylor, Mark P, and Helen Allen. 1992. "The Use of Technical Analysis in the Foreign Exchange Market." Journal of International Money and Finance 11 (3): 304-14. https://doi.org/https://doi.org/10.1016/0261-5606(92)90048-3.
HENDERSHOTT, TERRENCE, CHARLES M JONES, and ALBERT J MENKVELD. 2011. "Does Algorithmic Trading Improve Liquidity?" The Journal of Finance 66 (1): 1-33. https://doi.org/10.1111/j.1540-6261.2010.01624.x.
Hendershott, Terrence, and Ryan Riordan. 2009. "Algorithmic Trading and Information." https://econpapers.repec.org/RePEc:net:wpaper:0908.
Neely, Christopher J., 2002, The temporal pattern of trading rule returns and exchange rate intervention: Intervention does not generate technical trading rule profits, Journal of International Economics 58, 211-232
Osler, Carol L, 2005, Stop-loss orders and price cascades in currency markets, Journal of International Money and Finance 24, 219-241.
Chaboud, Alain, Ben Chiquoine, Erik Hjalmarsson, and Clara Vega, 2009, Rise of the machines: Algorithmic trading in the foreign exchange market, Working paper, Federal Reserve System.
Brock, William, Josef Lakonishok, and Blake LeBaron, 1992, Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance 47, 1731-1764.
Nick K. Lioudis, June 14, 2018 10:49 PM EDT, Strategies for part-time forex traders, https://www.investopedia.com/articles/forex/11/part-time-forex-strategies.asp.
Jean Folger, August 5, 2018 8:53 AM EDT Automated Trading Systems: The Pros and Cons, https://www.investopedia.com/articles/trading/11/automated-trading-systems.asp.
Crescenzio Gallo, July 20, 2014, The Forex Market in Practice: A Computing Approach for Automated Trading Strategies, https://www.omicsonline.org/open-access/the-forex-market-in-practice-a-computing-approach-for-automated-trading-strategies2162-6359.1000169.php?aid=28656.
Seznam doporučené literatury
Taylor, Mark P, and Helen Allen. 1992. "The Use of Technical Analysis in the Foreign Exchange Market." Journal of International Money and Finance 11 (3): 304-14. https://doi.org/https://doi.org/10.1016/0261-5606(92)90048-3.
HENDERSHOTT, TERRENCE, CHARLES M JONES, and ALBERT J MENKVELD. 2011. "Does Algorithmic Trading Improve Liquidity?" The Journal of Finance 66 (1): 1-33. https://doi.org/10.1111/j.1540-6261.2010.01624.x.
Hendershott, Terrence, and Ryan Riordan. 2009. "Algorithmic Trading and Information." https://econpapers.repec.org/RePEc:net:wpaper:0908.
Neely, Christopher J., 2002, The temporal pattern of trading rule returns and exchange rate intervention: Intervention does not generate technical trading rule profits, Journal of International Economics 58, 211-232
Osler, Carol L, 2005, Stop-loss orders and price cascades in currency markets, Journal of International Money and Finance 24, 219-241.
Chaboud, Alain, Ben Chiquoine, Erik Hjalmarsson, and Clara Vega, 2009, Rise of the machines: Algorithmic trading in the foreign exchange market, Working paper, Federal Reserve System.
Brock, William, Josef Lakonishok, and Blake LeBaron, 1992, Simple technical trading rules and the stochastic properties of stock returns, Journal of Finance 47, 1731-1764.
Nick K. Lioudis, June 14, 2018 10:49 PM EDT, Strategies for part-time forex traders, https://www.investopedia.com/articles/forex/11/part-time-forex-strategies.asp.
Jean Folger, August 5, 2018 8:53 AM EDT Automated Trading Systems: The Pros and Cons, https://www.investopedia.com/articles/trading/11/automated-trading-systems.asp.
Crescenzio Gallo, July 20, 2014, The Forex Market in Practice: A Computing Approach for Automated Trading Strategies, https://www.omicsonline.org/open-access/the-forex-market-in-practice-a-computing-approach-for-automated-trading-strategies2162-6359.1000169.php?aid=28656.
Přílohy volně vložené
KARIN KNORR CETINA and URS BRUEGGE, 2002, Global Microstructures: The Virtual Societies of Financial Markets, Vol. 107, No. 4 (January 2002), pp. 905-950 Published by: The University of Chicago Press, DOI: 10.1086/341045
JOHN C. HULL, 2012, Options futures and other derivatives, pearson, ISBN 978-0-13-216494-8
GATEV EVAN, WILLIAM N GOETZMANN, K GEERT ROUWENHORST, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule." Review of Financial Studies. Volume 19, Issue 3, Fall 2006, Pages 797-827, https://doi.org/10.1093/rfs/hhj020
ANDREI A. KIRILENKO and ANDREW W. LO, Journal of Economic Perspectives, Volume 27, Number 2Spring 2013Pages 51-72, Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents, https://doi.org/10.1093/rfs/hhj020
ALAIN P. CHABAUD, BENJAMIN CHIQUOINE, ERIK HJALMARSSON and CLARA VEGA, 2014, Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market, Journal of Finance, 2014, vol. 69, issue 5, 2045-2084,
HENDERSHOTT TERRENCE, CHARLES M. JONES and ALBERT J MENKVELD. 2011. "Does Algorithmic Trading Improve Liquidity?" The Journal of Finance 66 (1): 1-33. https://doi.org/10.1111/j.1540-6261.2010.01624.x.
YESHA YADAV, 2015, How Algorithmic Trading Undermines Efficiency in Capital Markets, Vanderbilt Law and Economics Research Paper No. 15-7, 65 Pages, ISSN: 0042-2533.
BOMING HUANG, YUXIANG HUAN, LI DA XX, LIRONG ZHENG and ZHUO ZOU, 2018, "Automated Trading Systems Statistical and Machine Learning