The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.

An Agent-Based Artificial Market Model for Studying the Bitcoin Trading

Luisanna Cocco;Roberto Tonelli;Michele Marchesi
2019-01-01

Abstract

The objective of this paper is to simulate the trading of the currency pair BTC/USD, investigating through the theory of the genetic algorithms the best sets of trading strategies, simulating through a realistic order book the bitcoin price formation, and reproducing a bitcoin price series that exhibits some stylized facts found in real-time price series. In this artificial market model two kinds of agents, Chartists and Random traders, perform trading. Chartists trade through the application of trading rules. Specifically, a part of Chartists trades applying the best sets of trading rules selected by a genetic algorithm that simulates a trading system, based on four technical analysis indicators, searching for parameters of each indicator that guarantee the highest profits in the training period; the remaining part trades applying trading rules choosing their parameters in a random way. On the contrary random trader's trade without applying any trading strategy, issuing in a random way sell or buy orders. Results show that the best sets of rules found to guarantee the highest profits both in the training and in the testing periods, and perform well also in the artificial market model where the Chartists who adopt the best sets of trading rules are able to achieve higher profits.
Artificial cryptocurrency market; Genetic algorithm; Simulation; Trading strategies; Bitcoin; Genetic algorithms; Computational modeling; Training; Indexes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11584/263590
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