Application of Machine Learning: Automated Trading Informed by Event Driven Data by In this paper, we build trading strategies by applying machine-learning techniques to both technical analysis indicators and market senti- the data set is fit to nonlinear models using machine learning algorithms such as ArtificialNeuralNetworks. File Size: 1MB. Mar 21, · In this research, the authors create an algorithmic trading strategy that attempts to predict the price of Bitcoin in a variety of minute intervals. Three models were used: a simple logistic regression model, a logistic regression model after Principal Component Analysis, and lastly a model using a neural network with one hidden layer. The researchers [ ]. To the best of our knowledge, this is the first work that tries to investigate applying machine learning methods for the purpose of creating trading strategies on the Bitcoin market. EMA results.
Application of machine learning algorithms for bitcoin automated trading pdf[PDF] Automated Bitcoin Trading via Machine Learning Algorithms | Semantic Scholar
Results Citations. Topics from this paper. Machine learning Random forest. Generalized linear model Experiment. Citation Type. Has PDF. Publication Type. More Filters. View 2 excerpts, cites methods. Research Feed. View 1 excerpt, cites methods. Impact of graph-based features on Bitcoin prices. View 2 excerpts, cites methods and background.
Prediction of Bitcoin Price using Data Mining. Quantitative cryptocurrency trading: exploring the use of machine learning techniques. In the second variation Principal Component Analysis, or PCA , was used to find the correlations between the features and remove the noise from the data. The last variation of the strategy used a neural network with a single hidden layer and a rectifier. The results of strategy variations were based on three main metrics: weighted average, gains, and area under the curve AUC which measures the true positive versus the false positive rate.
All three models had gains that significantly outperformed the average increase per minute on bitcoin suggesting that the use of these strategies in a live trading environment could potentially be more successful than just buying and holding Bitcoin. The first model that used weighted linear regression worked well to achieve gains and the PCA model worked well to remove some of the noise in the data.
The model that used a neural network consistently outperformed the weighted average metric and the AUC metric. Risk Warning: The FXCM Group does not guarantee accuracy and will not accept liability for any loss or damage which arise directly or indirectly from use of or reliance on information contained within the webinars.
The FXCM Group may provide general commentary which is not intended as investment advice and must not be construed as such. Please ensure that you fully understand the risks involved. To read in more depth about the Bitcoin algorithm, review the research paper by Justin Xu and Dhruv Medarametla, click here.