This research tries to establish to what extent three popular algorithmic systems for trading financial assets: the relative strength index, the moving average convergence diversion (MACD) and the pivot reversal (PR), are suitable for Bitcoin trading. Using data about daily Bitcoin prices from the beginning of April 2013 until the end of October 2018, we explored these strategies through particle swarm optimization. Our results demonstrate that the relative strength index produced poorer results than the buy and hold strategy. In contrast, the MACD and PR strategies dramatically outperformed the buy and hold strategy. However, our optimizing process produced even better results.
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Round numbers such as $1000 and $6000 per 1 Bitcoin.
State-issued money that is neither convertible by law to any other thing, nor fixed in value in terms of any objective standard.
"pbest" = The setup that achieved the best results in reducing the maximum drawdown and maximizing the percentage of profitable trades, the profit factor and the net profit.
"gbest" = global best identification.
Meaning that the maximum percentage drawdown of the investment equal 3%.
Meaning that 48% of all trades are profitable.
Meaning that the gross profits exceed the gross losses by 20%.
The weights that were chosen for each type of investor are only an example of the possible weights.
Risk-averse investors, risk-neutral investors and risk seekers.
As opposed to Wilder (1978), who suggested 14, 30, and 70 setups.
A long position means buying rather than selling a financial asset.
A short position means selling rather than buying a financial asset.
Support and resistance levels are the lowest and highest prices a financial asset has reached in a specific period of time.
Bull markets refer to upward trends.
Bear markets refer to downward trends.
Swing traders’ investment horizons vary from a few days to a few months.
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Cohen, G. Optimizing Algorithmic Strategies for Trading Bitcoin. Comput Econ 57, 639–654 (2021). https://doi.org/10.1007/s10614-020-09972-6