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Makar Vorobyov
Makar Vorobyov

7 Techniques To Successful Crypto Trading.pdf WORK


In the last three years, there has been an increasing interest on forecasting and profiting from cryptocurrencies with ML techniques. Table 1 summarizes several of those papers, presented in chronological order since the work of Madan et al. (2015), which, to the best of our knowledge, is one of the first works to address this issue. We do not intend to provide a complete list of papers for this strand of literature; instead, our aim is to contextualize our research and to highlight its main contributions. For a comprehensive survey on cryptocurrency trading and many more references on ML trading, see, for example, Fang et al. (2020).




7 Techniques to Successful Crypto Trading.pdf


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From the list in Table 1, studies that are closer to the research conducted here are Ji et al. (2019b), where the main goal is to compare several ML techniques, and Borges and Neves (2020), where the main goal is to show that assembling ML algorithms with different data resampling methods generate profitable trading strategies in the cryptocurrency markets. The main differences between our research and the first paper are that we consider not only bitcoin but also, ethereum and litecoin, and we also consider trading costs. Meanwhile, the main differences with the second paper are that we study daily returns and use blockchain features in the input set instead of one-minute returns and technical indicators.


This study examines the predictability of the returns of major cryptocurrencies and the profitability of trading strategies supported by ML techniques. The framework considers several classes of models, namely, linear models, random forests (RFs), and support vector machines (SVMs). These models are used not only to produce forecasts of the dependent variable, which is the returns of the cryptocurrencies (regression models), but also to produce binary buy or sell trading signals (classification models).


The win rates of the strategies are never lower than 50%, with the best results achieved by Ensembles 5 and 6 for ethereum, at 60.71% and 63.33%, respectively, but the mean daily returns are not impressively high. Generally, these strategies are able to significantly beat the market. Additionally, these trading strategies are subjected to a high tail risk, with CVaRs at 1% between 3.88% and 13.40% and maximum drawdown between 11.15% and 48.06%. Basically, the results point out that the best trading strategies are Ensemble 5 applied to ethereum and litecoin, which achieved an annualized Sharpe ratio of 80.17% and 91.35% and an annualized return, after proportional trading costs of 0.5%, of 9.62% and 5.73%, respectively. These values seem low when compared with the daily minima and maxima returns of these cryptocurrencies during the test sub-sample. However, one may argue that the fact that they are positive may support the belief that ML techniques have potential in the cryptocurrencies market, that is, when prices are falling down, and the probability of extreme negative events is high, the trading strategy still presents a positive return after trading costs, which may indicate that these strategies may hold even in quite adverse market conditions.


There are many techniques day traders use to make gains on short-term fluctuations in the crypto markets. A crypto day trader should devise a winning strategy backed by research, with well-laid plans for when to enter and exit their positions.


For prospective day traders, certain websites allow users to track and copy the most successful traders on the platform. Below are some of the most popular trading strategies in the crypto day trading game.


Range trading capitalizes on sideways markets (or non-trending markets) by pinpointing stable high and low prices, represented on charts as resistance and support levels. Day traders using range trading techniques identify a period of time to buy a crypto asset when it is oversold (at a low price) and sell when it is overbought (at a higher price) to make profits. 041b061a72


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