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In the ever-evolving landscape of blockchain technology, Bitcoin reigns supreme, accounting for a staggering 40% of the total crypto market cap. To capitalize on this opportunity, we embarked on a project to predict Bitcoin prices near-real time basis

In this blog post, we will show you how to unleash the full power of Databricks to process vast amounts of data, develop cutting-edge machine learning models, and make near accurate predictions for Bitcoin prices.

We implemented a 3-step approach a) market sentiment analysis, b) on-chain metrics, and 3) past price trends to unlock the true potential of Bitcoin investments.

• To begin with, we utilized Databricks to develop a sentiment model that could scan all tweets related to Bitcoin and gauge the overall sentiment of the community. This model helped us understand the pulse of the market and make predictions based on the collective mood of the Bitcoin community. We ingested the tweet data into Delta Lake, an ultra-fast and highly-scalable data lake solution provided by Databricks. With its high-performance data processing engine, Delta Lake made it a breeze to manage and process massive amounts of data in real-time

Visualisation shows majority prevalence of neutral sentiment among market participants in last hour.

• Next we utilized Databricks' cutting-edge machine learning capabilities to develop a regressive model that analysed a range of on-chain metrics, including the US dollar and Nasdaq 100 Futures Ticker, gold prices, and the Bitcoin fear and greed index. We Levered Databricks' extensive machine learning libraries and tools, such as MLflow for model tracking and management and XGBoost for gradient boosting-based regression, we were able to create a powerful model that accurately predicted Bitcoin prices based on on-chain activity. By incorporating additional factors such as gold, Nasdaq 100, and the US dollar, we gained a deeper understanding of state of financial markets and demand for Bitcoin and the overall health of the network.

Screen capture shows Prediction based on Regression model which considers On-chain metrics and
other financial market metrics as input variables vs Actual price for 7 hours

• Finally, we utilized Databricks to develop a time-series model that could predict Bitcoin prices based on historical price trends. This model used past price data to make predictions about future prices. Databricks offers a range of time-series analysis tools, including Prophet and ARIMA models, which we utilized to create the time-series model. This model helped us understand the overall trend of the Bitcoin market and make predictions based on past performance. See Prediction and Diagram below for reference

Screen capture shows prediction of Time Series for Bitcoin model based on Prophet vs Actual price for 7 hours.

Conclusion:

Using Databricks, we were able to quickly generate baseline models and track different machine learning versions using MLflow. This enabled us to easily compare and evaluate various models and select the most effective one. Databricks also allowed us to effortlessly manage and process vast amounts of data from multiple sources, including the Twitter API and on-chain metrics, using Delta Lake. This made it easier to develop comprehensive models that could accurately predict Bitcoin prices.

Our project highlights the importance of utilizing a comprehensive approach to predict market trends and make informed investment decisions. As the blockchain industry continues to grow, we believe that our approach could be applied to other cryptocurrencies as well.

In summary, with the vast array of powerful tools and features offered by Databricks, we were able to take our project to unprecedented heights and make accurate predictions for Bitcoin prices.