Bitcoin Price Prediction
Bayesian regression for latent source model and Bitcoin
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Btcpredictor | 269 | 6 years ago | 3 | mit | Matlab | |||||
| Bitcoin price prediction algorithm using bayesian regression techniques | ||||||||||
| Bitcoin Price Prediction | 214 | 4 years ago | mit | Python | ||||||
| Bayesian regression for latent source model and Bitcoin | ||||||||||
| Bayesian Regression To Predict Bitcoin Price Variations | 19 | 7 years ago | 1 | Python | ||||||
| predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, you will have to familiarize yourself with a machine learning technique, Bayesian Regression, and implement this technique in Python | ||||||||||
| Bayesian Regression And Bitcoin | 11 | 6 years ago | Python | |||||||
| # Bayesian-Regression-to-Predict-Bitcoin-Price-Variations Predicting the price variations of bitcoin, a virtual cryptographic currency. These predictions could be used as the foundation of a bitcoin trading strategy. To make these predictions, we will have to familiarize ourself with a machine learning technique, Bayesian Regression, and implement this technique in Python. # Datasets We have the datasets in the data folder. The original raw data can be found here: http://api.bitcoincharts.com/v1/csv/. The datasets from this site have three attributes: (1) time in epoch, (2) price in USD per bitcoin, and (3) bitcoin amount in a transaction (buy/sell). However, only the first two attributes are relevant to this project. To make the data to have evenly space records, we took all the records within a 20 second window and replaced it by a single record as the average of all the transaction prices in that window. Not every 20 second window had a record; therefore those missing entries were filled using the prices of the previous 20 observations and assuming a Gaussian distribution. The raw data that has been cleaned is given in the file dataset.csv Finally, as discussed in the paper, the data was divided into a total of 9 different datasets. The whole dataset is partitioned into three equally sized (50 price variations in each) subsets: train1, train2, and test. The train sets are used for training a linear model, while the test set is for evaluation of the model. There are three csv files associated with each subset of data: *_90.csv, *_180.csv, and *_360.csv. In _90.csv, for example, each line represents a vector of length 90 where the elements are 30 minute worth of bitcoin price variations (since we have 20 second intervals) and a price variation in the 91st column. Similarly, the *_180.csv represents 60 minutes of prices and *_360.csv represents 120 minutes of prices. # Project Requirements We are expected to implement the Bayesian Regression model to predict the future price variation of bitcoin as described in the reference paper. The main parts to focus on are Equation 6 and the Predicting Price Change section. # Logic in bitcoin.py 1. Compute the price variations (Δp1, Δp2, and Δp3) for train2 using train1 as input to the Bayesian Regression equation (Equations 6). Make sure to use the similarity metric (Equation 9) in place of the Euclidean distance in Bayesian Regression (Equation 6). 2. Compute the linear regression parameters (w0, w1, w2, w3) by finding the best linear fit (Equation 8). Here you will need to use the ols function of statsmodels.formula.api. Your model should be fit using Δp1, Δp2, and Δp3 as the covariates. Note: the bitcoin order book data was not available, so you do not have to worry about the rw4 term. 3. Use the linear regression model computed in Step 2 and Bayesian Regression estimates, to predict the price variations for the test dataset. Bayesian Regression estimates for test dataset are computed in the same way as they are computed for train2 dataset – using train1 as an input. 4. Once the price variations are predicted, compute the mean squared error (MSE) for the test dataset (the test dataset has 50 vectors => 50 predictions). | ||||||||||
| Predicting Bitcoin Price Variations | 8 | 7 years ago | Python | |||||||
| Predicting Bitcoin Price Variations using Bayesian Regression | ||||||||||
| Bitcoin Prediction | 6 | 6 years ago | 1 | Python | ||||||
| Predicting Bitcoin Price Variations using Bayesian Regression | ||||||||||
| Bitpredict | 5 | 6 years ago | R | |||||||
| This project is concerned with predicting the price of Bitcoin using machine learning. The goal is to ascertain with what accuracy can the direction of Bitcoin price in USD can be predicted. The task is achieved with varying degrees of success through the implementation of Bayesian Regression.The popular ARIMA model for time series forecasting is implemented as a comparison to Holt’s Forecasting Model, exponential triple smoothing and Bayesian Regression. It can clearly be seen that Bayesian regression gives the best forecasting model for bitcoin price prediction. It has a very low Mean accuracy prediction error of 7.82 which is way lower compared to other models. The model is run only for 10 iterations or Markov chains and increasing this number would further improve the model accuracy. The implementation is done in R. | ||||||||||
| Bayesiantouchcriterion | 5 | 10 years ago | mit | Objective-C | ||||||
| Improves iOS target selection for finger touch using the Bayesian Touch Criterion. | ||||||||||
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