Background: Due to its decentralized nature and opportunity for substantial gains, cryptocurrency has become a popular investment opportunity. However, the highly unpredictable and volatile nature of the cryptocurrency market poses a challenge for investors looking to predict price movements and make profitable investments. Time series analysis, which recognizes trends and patterns in previous price data to create forecasts about future price movements, is one of the prominent and effective techniques for price prediction. Integrating Machine learning (ML) techniques and technical indicators along with time series analysis can enhance the prediction accuracy significantly.

Objectives: The objective of this thesis is to identify an effective ML algorithm for making long-term predictions of Bitcoin prices, by developing prediction models using the ML algorithms and making predictions using the technical indicators(RelativeStrength Index (RSI), Exponential Moving Average (EMA), Simple Moving Average (SMA)) as input for these models.

Method: A Systematic Literature Review (SLR) has been employed to identify effective ML algorithms for making long-term predictions of cryptocurrency prices and conduct an experiment on these identified algorithms. The selected algorithms are trained and tested using the technical indicators RSI, EMA, and SMA calculated using the historic price data over a period of May 2017 to May 2023 taken fromCoinGecko API. The models are then evaluated using various metrics and the effect of the indicators on the performance of the prediction models is found using permutation feature importance and correlation analysis.

Results: After conducting SLR, the ML algorithms Random Forest (RF), GradientBoosting (GB), Long Short-Term Memory (LSTM), and Gated Recurrent Unit(GRU) have been identified as effective algorithms to conduct our experiment on. Out of these algorithms, LSTM has been found to be the most accurate model out of the 4 selected algorithms based on Root Mean Square Error (RMSE) score(0.01083), Mean Square Error (MSE) score (0.00011), Coefficient of Determination (R2) score (0.80618), Time-Weighted Average (TWAP) score (0.40507), and Volume-Weighted Average (VWAP) score (0.35660) respectively. Also, by performing permutation feature importance and correlation analysis it was found that the moving averages EMA and SMA had a greater impact on the performance of all the prediction models as compared to RSI.

Conclusion: Prediction models were built using the chosen ML algorithms identified through the literature review. Based on the dataset built from the data collected through the CoinGecko database and taking technical indicators as the input features, models were trained and tested using the chosen ML algorithms. The LSTM prediction algorithm was found to be the most accurate out of the chosen algorithms based on the RMSE, R2, TWAP, and VWAP scores obtained.