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10.1186/s40854-020-00217-x http://scihub22266oqcxt.onion/10.1186/s40854-020-00217-x C7785332Forecastingandtradingcryptocurrencieswithmachinelearningunderchangingmarketconditions.!7785332!35024269     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Financ+Innov 2021 ; 7 (1): ä Nephropedia Template TP {{tp|p=35024269|t=2021. Forecasting and trading cryptocurrencies with machine learning under changing market conditions |pdf=|usr=}}{{35024269}} gab.com Text Forecasting and trading cryptocurrencies with machine learning under changing market conditions JO: Financ Innov http://www.kidney.de/pget.php?&tw=1&pmid=35024269 #FOAvip This study examines the predictability of three major cryptocurrencies?bitcoin, ethereum, and litecoin?and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions. Twit Text FOAVip Forecasting and trading cryptocurrencies with machine learning under changing market conditions ????Financ Innov http://www.kidney.de/pget.php?&tw=1&pmid=35024269 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=35024269 #FOAvip English Wikipedia <ref>{{Cite pmid|35024269}}</ref> Forecasting and trading cryptocurrencies with machine learning under changing market conditions #MMPMID35024269 Sebastião H; Godinho P Financ Innov 2021[]; 7 (1): ä PMID35024269show ga This study examines the predictability of three major cryptocurrencies?bitcoin, ethereum, and litecoin?and the profitability of trading strategies devised upon machine learning techniques (e.g., linear models, random forests, and support vector machines). The models are validated in a period characterized by unprecedented turmoil and tested in a period of bear markets, allowing the assessment of whether the predictions are good even when the market direction changes between the validation and test periods. The classification and regression methods use attributes from trading and network activity for the period from August 15, 2015 to March 03, 2019, with the test sample beginning on April 13, 2018. For the test period, five out of 18 individual models have success rates of less than 50%. The trading strategies are built on model assembling. The ensemble assuming that five models produce identical signals (Ensemble 5) achieves the best performance for ethereum and litecoin, with annualized Sharpe ratios of 80.17% and 91.35% and annualized returns (after proportional round-trip trading costs of 0.5%) of 9.62% and 5.73%, respectively. These positive results support the claim that machine learning provides robust techniques for exploring the predictability of cryptocurrencies and for devising profitable trading strategies in these markets, even under adverse market conditions. äForecasting and trading cryptocurrencies with machine learning under c(epmc).pdf DeepDyve Pubget Overpricing