What question did this study set out to answer?

To assess the predictability of Bitcoin, Ethereum, and Litecoin and the effectiveness of machine learning-based trading strategies under varying market conditions.

January 6, 2021SHILAP Revista de lepidopterologíaOpen Access

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

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To assess the predictability of Bitcoin, Ethereum, and Litecoin and the effectiveness of machine learning-based trading strategies under varying market conditions.
Examined three cryptocurrencies: Bitcoin, Ethereum, and Litecoin.
Utilized machine learning techniques, including linear models, random forests, and support vector machines.
Tested trading strategies from August 15, 2015, to March 03, 2019, with a validation period reflecting market changes.
Ensemble model produced identical signals with Sharpe ratios of 80.17% for Ethereum and 91.35% for Litecoin.
Annualized returns observed after trading costs were 9.62% for Ethereum and 5.73% for Litecoin.
Five out of 18 models had success rates below 50% during the test period.

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Implication

Randomized trial examines predictability of cryptocurrencies and profitability of trading strategies using machine learning, suggesting robust results under adverse conditions.

Cite This Study

Sebastião et al. (2021) studied this question.

synapsesocial.com/papers/69dac49baae38ff6ad8368d8 https://doi.org/10.1186/s40854-020-00217-x

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Implication

Randomized trial examines predictability of cryptocurrencies and profitability of trading strategies using machine learning, suggesting robust results under adverse conditions.

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Key Points

Discussion

Implication

Cite This Study

Forecasting and trading cryptocurrencies with machine learning under changing market conditions

Key Points

Discussion

Implication

Cite This Study

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